Agent Architecture: Building AI-Powered Development Harnesses

TL;DR: Claude Code is not a chat box with file access. It is a programmable runtime with 29 documented lifecycle events, each hookable with shell scripts the model cannot skip. Stack hooks into dispatchers, dispatchers into skills, skills into agents, agents into workflows, and you get an autonomous development harness that enforces constraints, delegates work, persists memory across sessions, and orchestrates multi-agent deliberation. This guide covers every layer of that stack: from a single hook to a 10-agent consensus system. Zero frameworks required. All bash and JSON.

Andrej Karpathy coined a term for what grows around an LLM agent: claws. The hooks, scripts, and orchestration that let the agent grip the world outside its context window.¹ Most developers treat AI coding agents as interactive assistants. They type a prompt, watch it edit a file, and move on. That framing caps productivity at whatever you can personally oversee.

The infrastructure mental model is different: an AI coding agent is a programmable runtime with an LLM kernel. Every action the model takes passes through hooks you control. You define policies, not prompts. The model operates within your infrastructure the same way a web server operates within nginx rules. You do not sit at nginx and type requests. You configure it, deploy it, and monitor it.

The distinction matters because infrastructure compounds. A hook that blocks credentials in bash commands protects every session, every agent, every autonomous run. A skill that encodes your evaluation rubric applies consistently whether you invoke it or an agent does. An agent that reviews code for security runs the same checks whether you are watching or not.²

Key Takeaways

• Hooks guarantee execution; prompts do not. Use hooks for linting, formatting, security checks, and anything that must run every time regardless of model behavior. Exit code 2 blocks actions. Exit code 1 only warns.³
• Skills encode domain expertise that auto-activates. The description field determines everything. Claude uses LLM reasoning (not keyword matching) to decide when to apply a skill.⁴
• Subagents prevent context bloat. Isolated context windows for exploration and analysis keep the main session lean. Run independent subagents in parallel, and use agent teams when workers need sustained coordination.⁵
• Memory lives in the filesystem. Files persist across context windows. CLAUDE.md, MEMORY.md, rules directories, and handoff documents form a structured external memory system.⁶
• Multi-agent deliberation catches blind spots. Single agents cannot challenge their own assumptions. Two independent agents with different evaluation priorities catch structural failures that quality gates cannot address.⁷
• The harness pattern is the system. CLAUDE.md, hooks, skills, agents, and memory are not independent features. They compose into a deterministic layer between you and the model that scales with automation.

How to Use This Guide

Each section builds on the previous. The Decision Framework at the end provides a lookup table for choosing the right mechanism for each problem type.

Five-Minute Golden Path

Before the deep dive, here is the shortest path from zero to a working harness. One hook, one skill, one subagent, one outcome.

Step 1: Create a security hook (2 minutes)

Create .claude/hooks/block-secrets.sh:

#!/bin/bash
INPUT=$(cat)
CMD=$(echo "$INPUT" | jq -r '.tool_input.command // empty')
if echo "$CMD" | grep -qEi '(AKIA|sk-|ghp_|password=)'; then
    echo "BLOCKED: Potential secret in command" >&2
    exit 2
fi

Wire it in .claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [{ "type": "command", "command": ".claude/hooks/block-secrets.sh" }]
      }
    ]
  }
}

Result: Every bash command Claude runs is now screened for leaked credentials. The model cannot skip this check.

Step 2: Create a code review skill (1 minute)

Create .claude/skills/reviewer/SKILL.md with frontmatter (name: reviewer, description: Review code for security issues, bugs, and quality problems. Use when examining changes, reviewing PRs, or auditing code., allowed-tools: Read, Grep, Glob) and a checklist: SQL injection, XSS, hardcoded secrets, missing error handling, functions over 50 lines.

Result: Claude auto-activates this expertise whenever you mention review, check, or audit.

Step 3: Spawn a subagent (30 seconds)

In any Claude Code session, ask Claude to review the last 3 commits for security issues using a separate agent. Claude spawns an Explore agent that reads the diff, applies your review skill, and returns a summary. Your main context stays clean.

What you now have

A three-layer harness: a deterministic security gate (hook), domain expertise that auto-activates (skill), and isolated analysis that protects your context (subagent). Every section below expands one of these three layers.

Why Agent Architecture Matters

Simon Willison frames the current moment around a single observation: writing code is cheap now.⁸ Correct. But the corollary is that verification is now the expensive part. Cheap code without verification infrastructure produces bugs at scale. The investment that pays off is not a better prompt. It is the system around the model that catches what the model misses.

Three forces make agent architecture necessary:

Context windows are finite and lossy. Every file read, tool output, and conversation turn consumes tokens. Microsoft Research and Salesforce tested 15 LLMs across 200,000+ simulated conversations and found a 39% average performance drop from single-turn to multi-turn interaction.⁹ The degradation starts in as few as two turns and follows a predictable curve: precise multi-file edits in the first 30 minutes degrade into single-file tunnel vision by minute 90. Longer context windows do not fix this. The same study’s “Concat” condition (full conversation as a single prompt) achieved 95.1% of single-turn performance with identical content. The degradation comes from turn boundaries, not token limits.

Model behavior is probabilistic, not deterministic. Telling Claude “always run Prettier after editing files” works roughly 80% of the time.³ The model might forget, prioritize speed, or decide the change is “too small.” For compliance, security, and team standards, 80% is not acceptable. Hooks guarantee execution: every Edit or Write triggers your formatter, every time, no exceptions. Deterministic beats probabilistic.

Single perspectives miss multi-dimensional problems. A single agent reviewing an API endpoint checked authentication, validated input sanitization, and verified CORS headers. Clean bill of health. A second agent, prompted separately as a penetration tester, found the endpoint accepted unbounded query parameters that could trigger denial-of-service through database query amplification.⁷ The first agent never checked because nothing in its evaluation framework treated query complexity as a security surface. That gap is structural. No amount of prompt engineering fixes it.

Agent architecture addresses all three: hooks enforce deterministic constraints, subagents manage context isolation, and multi-agent orchestration provides independent perspectives. Together they form the harness.

The Harness Pattern

The harness is not a framework. It is a pattern: a composable set of files, scripts, and conventions that wrap an AI coding agent in deterministic infrastructure. The components:

┌──────────────────────────────────────────────────────────────┐
│                      THE HARNESS PATTERN                      │
├──────────────────────────────────────────────────────────────┤
│  ORCHESTRATION                                                │
│  ┌────────────┐  ┌────────────┐  ┌────────────┐             │
│  │   Agent     │  │   Agent    │  │  Consensus │             │
│  │   Teams     │  │  Spawning  │  │  Validation│             │
│  └────────────┘  └────────────┘  └────────────┘             │
│  Multi-agent deliberation, parallel research, voting          │
├──────────────────────────────────────────────────────────────┤
│  EXTENSION LAYER                                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐    │
│  │  Skills   │  │  Hooks   │  │  Memory  │  │  Agents  │    │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘    │
│  Domain expertise, deterministic gates, persistent state,     │
│  specialized subagents                                        │
├──────────────────────────────────────────────────────────────┤
│  INSTRUCTION LAYER                                            │
│  ┌──────────────────────────────────────────────────────┐    │
│  │     CLAUDE.md  +  .claude/rules/  +  MEMORY.md       │    │
│  └──────────────────────────────────────────────────────┘    │
│  Project context, operational policy, cross-session memory    │
├──────────────────────────────────────────────────────────────┤
│  CORE LAYER                                                   │
│  ┌──────────────────────────────────────────────────────┐    │
│  │           Main Conversation Context (LLM)             │    │
│  └──────────────────────────────────────────────────────┘    │
│  Your primary interaction; finite context; costs money        │
└──────────────────────────────────────────────────────────────┘

Instruction Layer: CLAUDE.md files and rules directories define what the agent knows about your project. They load automatically at session start and after every compaction. This is the agent’s long-term architectural memory.

Extension Layer: Skills provide domain expertise that auto-activates based on context. Hooks provide deterministic gates that fire on every matching tool call. Memory files persist state across sessions. Custom agents provide specialized subagent configurations.

Orchestration Layer: Multi-agent patterns coordinate independent agents for research, review, and deliberation. Spawn budgets prevent runaway recursion. Consensus validation ensures quality.

The key insight: most users work entirely in the Core Layer, watching context bloat and costs climb. Power users configure the Instruction and Extension layers, then use the Core Layer only for orchestration and final decisions.²

Managed vs. Self-Hosted Harnesses (April 2026)

Throughout early 2026, the “build your own harness” path was the only real option. In April 2026, that changed. Anthropic shipped Claude Managed Agents in public beta (April 8): harness loop + tool execution + sandbox container + state persistence as a REST API, billed at standard tokens plus $0.08/session-hour. OpenAI’s Agents SDK update (April 16) formalized the same split — harness and compute as separate layers, with native sandbox providers (Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, Vercel) and snapshot/rehydrate for surviving container loss.²³²⁴

The deeper SDK surface for the OpenAI side landed in openai-agents Python v0.14.0 (released April 15, 2026; announced April 16): a SandboxAgent subclass of Agent with default_manifest, sandbox instructions, and capabilities; a Manifest describing the fresh-workspace contract (files, dirs, local files, Git repos, env, users, mounts); a SandboxRunConfig for per-run wiring of sandbox client, live session injection, manifest overrides, snapshots, and materialization concurrency limits. Built-in capabilities cover shell access, filesystem editing, image inspection, skills, sandbox memory, and compaction. Sandbox memory persists extracted lessons across runs and progressively discloses them; workspaces support local files, Git repo entries, and remote mounts (S3, R2, GCS, Azure Blob, S3 Files); snapshots are portable across providers. Backends: UnixLocalSandboxClient, DockerSandboxClient, and hosted clients for Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, and Vercel via optional extras.²⁴

For Python projects that want to embed the Claude Code runtime as a library — between “shell out to claude” and “REST API to Managed Agents” — claude-agent-sdk-python is the third option. The April 28-29 series (v0.1.69 → v0.1.71) bumped the bundled CLI to v2.1.123, raised the floor on the mcp dependency to >=1.19.0 (older versions silently dropped CallToolResult returns from in-process MCP tools, leaving the model with a validation-error blob), and brought SandboxNetworkConfig to schema parity with the TypeScript SDK (allowedDomains, deniedDomains, allowManagedDomainsOnly, allowMachLookup).³⁰

The architectural fork is now real:

When to pick which: self-hosted remains right for teams that already have infrastructure muscle, want skills/hooks they control, or are optimizing a specific workflow deeply. Managed is right for teams without dedicated platform engineers, when time-to-value matters more than customization, or when agent runs need to survive laptop closures reliably without you building that persistence layer. The two are compatible — you can run a self-hosted harness that delegates specific long-running tasks to Managed Agents via its REST API.

What the Harness Looks Like on Disk

~/.claude/
├── CLAUDE.md                    # Personal global instructions
├── settings.json                # User-level hooks and permissions
├── skills/                      # Personal skills (44+)
│   ├── code-reviewer/SKILL.md
│   ├── security-auditor/SKILL.md
│   └── api-designer/SKILL.md
├── agents/                      # Custom subagent definitions
│   ├── security-reviewer.md
│   └── code-explorer.md
├── rules/                       # Categorized rule files
│   ├── security.md
│   ├── testing.md
│   └── git-workflow.md
├── hooks/                       # Hook scripts
│   ├── validate-bash.sh
│   ├── auto-format.sh
│   └── recursion-guard.sh
├── configs/                     # JSON configuration
│   ├── recursion-limits.json
│   └── deliberation-config.json
├── state/                       # Runtime state
│   ├── recursion-depth.json
│   └── agent-lineage.json
├── handoffs/                    # Session handoff documents
│   └── deliberation-prd-7.md
└── projects/                    # Per-project memory
    └── {project}/memory/MEMORY.md

.claude/                         # Project-level (in repo)
├── CLAUDE.md                    # Project instructions
├── settings.json                # Project hooks
├── skills/                      # Team-shared skills
├── agents/                      # Team-shared agents
└── rules/                       # Project rules

Every file in this structure serves a purpose. The ~/.claude/ tree is personal infrastructure that applies to all projects. The .claude/ tree in each repository is project-specific and shared via git. Together, they form the complete harness.

Skills System

Skills are model-invoked extensions. Claude discovers and applies them automatically based on context, without you explicitly calling them.⁴ The moment you catch yourself re-explaining the same context across sessions is the moment you should build a skill.

When to Build a Skill

Skills are for knowledge Claude always has available. Slash commands are for actions you explicitly trigger. If you are deciding between the two, ask: “Should Claude apply this automatically, or should I decide when to run it?”

Creating a Skill

Skills live in four possible locations, from broadest to narrowest scope:⁴

Every skill requires a SKILL.md file with YAML frontmatter:

---
name: code-reviewer
description: Review code for security vulnerabilities, performance issues,
  and best practice violations. Use when examining code changes, reviewing
  PRs, analyzing code quality, or when asked to review, audit, or check code.
allowed-tools: Read, Grep, Glob
---

# Code Review Expertise

## Security Checks
When reviewing code, verify:

### Input Validation
- All user input sanitized before database operations
- Parameterized queries (no string interpolation in SQL)
- Output encoding for rendered HTML content

### Authentication
- Session tokens validated on every protected endpoint
- Permission checks before data mutations
- No hardcoded credentials or API keys in source

Frontmatter Reference

The Description Field Is Everything

At session start, Claude Code extracts every skill’s name and description and injects them into Claude’s context. When you send a message, Claude uses language model reasoning to decide if any skill is relevant. Independent analysis of the Claude Code source confirms the mechanism: skill descriptions are injected into an available_skills section of the system prompt, and the model uses standard language understanding to select relevant skills.¹⁰

Bad description:

description: Helps with code

Effective description:

description: Review code for security vulnerabilities, performance issues,
  and best practice violations. Use when examining code changes, reviewing
  PRs, analyzing code quality, or when asked to review, audit, or check code.

The effective description includes: what it does (review code for specific issue types), when to use it (examining changes, PRs, quality analysis), and trigger phrases (review, audit, check) that users naturally type.

Context Budget

All skill descriptions share a context budget that scales dynamically at 1% of the context window, with a fallback of 8,000 characters.⁴ If you have many skills, keep each description concise and put the key use case first. You can override the budget via the SLASH_COMMAND_TOOL_CHAR_BUDGET environment variable,¹¹ but the better fix is shorter, more precise descriptions. Run /context during a session to check whether any skills are being excluded.

Supporting Files and Organization

Skills can reference additional files in the same directory:

~/.claude/skills/code-reviewer/
├── SKILL.md                    # Required: frontmatter + core expertise
├── SECURITY_PATTERNS.md        # Referenced: detailed vulnerability patterns
└── PERFORMANCE_CHECKLIST.md    # Referenced: optimization guidelines

Reference them from SKILL.md with relative links. Claude reads these files on-demand when the skill activates. Keep SKILL.md under 500 lines and move detailed reference material to supporting files.¹²

Sharing Skills via Git

Project skills (.claude/skills/ in the repo root) are shared via version control:⁴

mkdir -p .claude/skills/domain-expert
# ... write SKILL.md ...
git add .claude/skills/
git commit -m "feat: add domain-expert skill for payment processing rules"
git push

When teammates pull, they get the skill automatically. No installation, no configuration. This is the most effective way to standardize expertise across a team.

Skills as a Prompt Library

Beyond single-purpose skills, the directory structure works as an organized prompt library:

~/.claude/skills/
├── code-reviewer/          # Activates on: review, audit, check
├── api-designer/           # Activates on: design API, endpoint, schema
├── sql-analyst/            # Activates on: query, database, migration
├── deploy-checker/         # Activates on: deploy, release, production
└── incident-responder/     # Activates on: error, failure, outage, debug

Each skill encodes a different facet of your expertise. Together, they form a knowledge base that Claude draws from automatically based on context. A junior developer gets senior-level guidance without asking for it.

Skills Compose with Hooks

Skills can define their own hooks in frontmatter that activate only while the skill runs. This creates domain-specific behavior that does not pollute other sessions:²

---
name: deploy-checker
description: Verify deployment readiness. Use when preparing to deploy,
  release, or push to production.
hooks:
  PreToolUse:
    - matcher: Bash
      hooks:
        - type: command
          command: "bash -c 'INPUT=$(cat); CMD=$(echo \"$INPUT\" | jq -r \".tool_input.command\"); if echo \"$CMD\" | grep -qE \"deploy|release|publish\"; then echo \"DEPLOYMENT COMMAND DETECTED. Running pre-flight checks.\" >&2; fi'"
---

Philosophy skills auto-activate via SessionStart hooks, injecting quality constraints into every session without explicit invocation. The skill itself is knowledge. The hook is enforcement. Together, they form a policy layer.

Common Skill Mistakes

Too-broad descriptions. A git-rebase-helper skill that activates on any git-related prompt (rebases, merges, cherry-picks, even git status) pollutes context on 80% of sessions. The fix is either tightening the description or adding disable-model-invocation: true and requiring explicit /skill-name invocation.⁴

Too many skills competing for budget. More skills means more descriptions competing for the 1% context budget. If you notice skills not activating, check /context for excluded ones. Prioritize fewer, well-described skills over many vague ones.

Critical information buried in supporting files. Claude reads SKILL.md immediately but only accesses supporting files when needed. If critical information is in a supporting file, Claude might not find it. Put essential information in SKILL.md directly.⁴

SDK Skill Surface (May 8, 2026)

Self-hosted harnesses on claude-agent-sdk-python v0.1.77+ should use the skills option on ClaudeAgentOptions to declare available skills, not the legacy "Skill" value in allowed_tools.³⁷ The "Skill" shorthand is deprecated and the dedicated option gives Claude Code more structured information about which skills are available. Bundled CLI in v0.1.77 is v2.1.133.

Hook Architecture

Hooks are shell commands triggered by Claude Code lifecycle events.³ They run outside the LLM as plain scripts, not prompts interpreted by the model. The model wants to run rm -rf /? A 10-line bash script checks the command against a blocklist and rejects it before the shell ever sees it. The hook fires whether the model wants it to or not.

Available Events

Claude Code exposes 29 documented lifecycle events across eight categories as of this guide update. The event list grows with releases, so treat the reference docs as the source of truth and check the cheat sheet for the current full table before wiring production hooks:¹³

Exit Code Semantics

Exit codes determine whether hooks block actions:³

Critical: Every security hook must use exit 2, not exit 1. Exit 1 is a non-blocking warning. The dangerous command still executes. This is the most common hook mistake across teams.¹⁴

Hook Configuration

Hooks live in settings files. Project-level (.claude/settings.json) for shared hooks. User-level (~/.claude/settings.json) for personal hooks:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          {
            "type": "command",
            "command": ".claude/hooks/validate-bash.sh"
          }
        ]
      }
    ],
    "PostToolUse": [
      {
        "matcher": "Write|Edit",
        "hooks": [
          {
            "type": "command",
            "command": "bash -c 'if [[ \"$FILE_PATH\" == *.py ]]; then black --quiet \"$FILE_PATH\" 2>/dev/null; fi'"
          }
        ]
      }
    ]
  }
}

The matcher field filters an event-specific value. For tool events, it matches tool_name values such as Bash, Edit, Write, Read, Glob, Grep, MCP tool names like mcp__server__tool, or * for all tools. Simple names and |-separated lists are exact matches; values with other characters are JavaScript regular expressions. Some events do not support matchers and always fire when configured.¹³

Hook Input/Output Protocol

Hooks receive JSON on stdin with full context:

{
  "tool_name": "Bash",
  "tool_input": {
    "command": "npm test",
    "description": "Run test suite"
  },
  "session_id": "abc-123",
  "agent_id": "main",
  "agent_type": "main"
}

For advanced control, PreToolUse hooks can output JSON to modify tool input, inject context, or make permission decisions. Use the hookSpecificOutput wrapper — the older top-level decision/reason format is deprecated for PreToolUse:

{
  "hookSpecificOutput": {
    "hookEventName": "PreToolUse",
    "permissionDecision": "allow",
    "permissionDecisionReason": "Command validated and modified",
    "updatedInput": {
      "command": "npm test -- --coverage --ci"
    },
    "additionalContext": "Note: This database has a 5-second query timeout."
  }
}

Three Types of Guarantees

Before writing any hook, ask: what kind of guarantee do I need?¹⁴

Formatting guarantees ensure consistency after the fact. PostToolUse hooks on Write/Edit run your formatter after every file change. The model’s output does not matter because the formatter normalizes everything.

{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Write|Edit",
        "hooks": [
          {
            "type": "command",
            "command": "bash -c 'if [[ \"$FILE_PATH\" == *.py ]]; then black --quiet \"$FILE_PATH\" 2>/dev/null; elif [[ \"$FILE_PATH\" == *.js ]] || [[ \"$FILE_PATH\" == *.ts ]]; then npx prettier --write \"$FILE_PATH\" 2>/dev/null; fi'"
          }
        ]
      }
    ]
  }
}

Safety guarantees prevent dangerous actions before they execute. PreToolUse hooks on Bash inspect commands and block destructive patterns with exit code 2:

#!/bin/bash
# validate-bash.sh — block dangerous commands
INPUT=$(cat)
CMD=$(echo "$INPUT" | jq -r '.tool_input.command')

if echo "$CMD" | grep -qE "rm\s+-rf\s+/|git\s+push\s+(-f|--force)\s+(origin\s+)?main|git\s+reset\s+--hard|DROP\s+TABLE"; then
    echo "BLOCKED: Dangerous command detected: $CMD" >&2
    exit 2
fi

Quality guarantees validate state at decision points. PreToolUse hooks on git commit commands run your linter or test suite and block the commit if quality checks fail:

#!/bin/bash
# quality-gate.sh — lint before commit
INPUT=$(cat)
CMD=$(echo "$INPUT" | jq -r '.tool_input.command')

if echo "$CMD" | grep -qE "^git\s+commit"; then
    if ! LINT_OUTPUT=$(ruff check . --select E,F,W 2>&1); then
        echo "LINT FAILED -- fix before committing:" >&2
        echo "$LINT_OUTPUT" >&2
        exit 2
    fi
fi

Hook Types Beyond Shell Commands

Claude Code supports five hook types:¹³

Command hooks (type: "command") run shell scripts. Fast, deterministic, no token cost.

MCP tool hooks (type: "mcp_tool") call a tool on an already-connected MCP server. Use them when validation logic already lives behind an MCP boundary and does not need a separate shell script.

Prompt hooks (type: "prompt") send a single-turn prompt to a fast Claude model. The model returns { "ok": true } to allow or { "ok": false, "reason": "..." } to block. Use for nuanced evaluation that regex cannot express.

Agent hooks (type: "agent") spawn a subagent with tool access (Read, Grep, Glob) for multi-turn verification. They are experimental; prefer command hooks for production gates and reserve agent hooks for checks that genuinely require inspecting actual files or test output:

{
  "hooks": {
    "Stop": [
      {
        "hooks": [
          {
            "type": "agent",
            "prompt": "Verify all unit tests pass. Run the test suite and check results. $ARGUMENTS",
            "timeout": 120
          }
        ]
      }
    ]
  }
}

HTTP hooks (type: "http") send the event’s JSON input as a POST request to a URL and receive JSON back. Use for webhooks, external notification services, or API-based validation (v2.1.63+). Not supported for SessionStart events:

{
  "hooks": {
    "PostToolUse": [
      {
        "hooks": [
          {
            "type": "http",
            "url": "https://your-webhook.example.com/hook",
            "headers": { "Authorization": "Bearer $WEBHOOK_TOKEN" },
            "allowedEnvVars": ["WEBHOOK_TOKEN"],
            "timeout": 10
          }
        ]
      }
    ]
  }
}

Async Hooks

Hooks can run in the background without blocking execution. Add async: true for non-critical operations like notifications and logging:¹³

{
  "type": "command",
  "command": ".claude/hooks/notify-slack.sh",
  "async": true
}

Use async for notifications, telemetry, and backups. Never use async for formatting, validation, or anything that must complete before the next action.

Dispatchers Over Independent Hooks

Running seven hooks all firing on the same event, each reading stdin independently, creates race conditions. Two hooks writing to the same JSON state file concurrently will truncate the JSON. Every downstream hook that parses that file breaks.²

The fix: one dispatcher per event that runs hooks sequentially from cached stdin:

#!/bin/bash
# dispatcher.sh — run hooks sequentially with cached stdin
INPUT=$(cat)
HOOK_DIR="$HOME/.claude/hooks/pre-tool-use.d"

for hook in "$HOOK_DIR"/*.sh; do
    [ -x "$hook" ] || continue
    echo "$INPUT" | "$hook"
    EXIT_CODE=$?
    if [ "$EXIT_CODE" -eq 2 ]; then
        exit 2  # Propagate block
    fi
done

Debugging Hooks

Five techniques for debugging hooks that fail silently:¹⁴

1. Test scripts independently. Pipe sample JSON: echo '{"tool_input":{"command":"git commit -m test"}}' | bash your-hook.sh
2. Use stderr for debug output. Exit code 2 stderr is fed back to Claude as an error message. Non-blocking stderr (exit 1, 3, etc.) appears only in verbose mode (Ctrl+O).
3. Watch for jq failures. Wrong JSON paths return null silently. Test jq expressions against real tool input.
4. Verify exit codes. A PreToolUse hook that uses exit 1 provides zero enforcement while appearing to work.
5. Keep hooks fast. Hooks run synchronously. Keep all hooks under 2 seconds, ideally under 500ms.

SDK-Side Hook Event Streaming

Self-hosted harnesses built on claude-agent-sdk-python (v0.1.74+, May 6, 2026) can subscribe to hook events directly from the message stream rather than going through shell-script callbacks.³⁶ Set include_hook_events=True on ClaudeAgentOptions and HookEventMessage objects (PreToolUse, PostToolUse, Stop, and others) yield from the same iterator as assistant messages and tool results. This mirrors the TypeScript SDK’s includeHookEvents option; bundled CLI was bumped to v2.1.129 in the same release.

The event-stream pattern is the right fit when your harness already lives in Python and you want hook signals in the same control flow as model output. The shell-script hook contract (exit codes, stdin JSON, dispatchers) remains the right answer for harnesses that compose multiple tools, share hooks across Claude Code and Codex, or need exit-code semantics for blocking.

Effort and Session Provenance (May 7-8, 2026)

Two additions in Claude Code v2.1.132 and v2.1.133 give hooks and subprocesses better signal about their execution context:³⁸³⁹

• effort.level in hook input. Hooks now receive an effort.level JSON field on the same input that carries tool_input and session_id. The same value is exported as the $CLAUDE_EFFORT env var, so Bash commands can read it without parsing JSON. Use this to scale hook cost with effort tier: skip expensive validation on low, run the full security gate on xhigh or max.
• CLAUDE_CODE_SESSION_ID env var on Bash subprocesses. Bash tool subprocesses now see the same session_id value the hooks see, exposed as CLAUDE_CODE_SESSION_ID. This closes the provenance gap for tools that log per-session state and were previously unable to correlate subprocess events with hook events.

Both signals are available without code changes; existing hooks that ignore the new fields keep working.

Memory and Context

Every AI conversation operates within a finite context window. As the conversation grows, the system compresses earlier turns to make room for new content. The compression is lossy. Architectural decisions documented in turn 3 may not survive to turn 15.⁹

The Three Mechanisms of Multi-Turn Collapse

The MSR/Salesforce study identified three independent mechanisms, each requiring a different intervention:⁹

Strategy 1: Filesystem as Memory

The most reliable memory across context boundaries lives in the filesystem. Claude Code reads CLAUDE.md and memory files at the start of every session and after every compaction.⁶

~/.claude/
├── configs/           # 14 JSON configs (thresholds, rules, budgets)
│   ├── deliberation-config.json
│   ├── recursion-limits.json
│   └── consensus-profiles.json
├── hooks/             # 95 lifecycle event handlers
├── skills/            # 44 reusable knowledge modules
├── state/             # Runtime state (recursion depth, agent lineage)
├── handoffs/          # 49 multi-session context documents
├── docs/              # 40+ system documentation files
└── projects/          # Per-project memory directories
    └── {project}/memory/
        └── MEMORY.md  # Always loaded into context

The MEMORY.md file captures errors, decisions, and patterns across sessions. When you discover that ((VAR++)) fails with set -e in bash when VAR is 0, you record it. Three sessions later, when you encounter a similar integer edge case in Python, the MEMORY.md entry surfaces the pattern.¹⁵

Auto Memory (v2.1.32+): Claude Code automatically records and recalls project context. As you work, Claude writes observations to ~/.claude/projects/{project-path}/memory/MEMORY.md. Auto memory loads the first 200 lines into your system prompt at session start. Keep it concise and link to separate topic files for detailed notes.⁶

Strategy 2: Proactive Compaction

Claude Code’s /compact command summarizes the conversation and frees context space while preserving key decisions, file contents, and task state.¹⁵

When to compact: - After completing a distinct subtask (feature implemented, bug fixed) - Before starting a new area of the codebase - When Claude starts repeating or forgetting earlier context - Roughly every 25-30 minutes during intensive sessions

Custom compaction instructions in CLAUDE.md:

# Summary Instructions
When using compact, focus on:
- Recent code changes
- Test results
- Architecture decisions made this session

Strategy 3: Session Handoffs

For tasks spanning multiple sessions, create handoff documents that capture the full state:

## Handoff: Deliberation Infrastructure PRD-7
**Status:** Hook wiring complete, 81 Python unit tests passing
**Files changed:** hooks/post-deliberation.sh, hooks/deliberation-pride-check.sh
**Decision:** Placed post-deliberation in PostToolUse:Task, pride-check in Stop
**Blocked:** Spawn budget model needs inheritance instead of depth increment
**Next:** PRD-8 integration tests in tests/test_deliberation_lib.py

The Status/Files/Decision/Blocked/Next structure provides the successor session with full context at minimal token cost. Starting a new session with claude -c (continue) or reading the handoff document goes straight to implementation.¹⁵

Strategy 4: Fresh-Context Iteration (The Ralph Loop)

For sessions exceeding 60-90 minutes, spawn a fresh Claude instance per iteration. State persists through the filesystem, not through conversational memory. Each iteration gets the full context budget:¹⁶

Iteration 1: [200K tokens] -> writes code, creates files, updates state
Iteration 2: [200K tokens] -> reads state from disk, continues
Iteration 3: [200K tokens] -> reads updated state, continues
...
Iteration N: [200K tokens] -> reads final state, verifies criteria

Compare with a single long session:

Minute 0:   [200K tokens available] -> productive
Minute 30:  [150K tokens available] -> somewhat productive
Minute 60:  [100K tokens available] -> degraded
Minute 90:  [50K tokens available]  -> significantly degraded
Minute 120: [compressed, lossy]     -> errors accumulate

The fresh-context-per-iteration approach trades 15-20% overhead for the orient step (reading state files, scanning git history) against full cognitive resources per iteration.¹⁶ The cost-benefit calculation: for sessions under 60 minutes, a single conversation is more efficient. Beyond 90 minutes, fresh-context produces higher-quality output despite the overhead.

Strategy 5: Managed Memory Curation (Dreaming)

Anthropic’s Claude Managed Agents added Dreaming as a Research Preview on May 6, 2026.³⁵ Per Anthropic: “Dreaming is a scheduled process that reviews your agent sessions and memory stores, extracts patterns, and curates memories so your agents improve over time.”³⁵

Dreaming runs in the background between sessions, not on the critical path. It complements rather than replaces the filesystem-as-memory pattern: your MEMORY.md file remains the load-bearing surface; Dreaming writes curated memory entries into the Managed Agents memory store, which the agent reads at session start. The two patterns coexist for harnesses that mix self-hosted filesystem state with managed-side curation.

Dreaming is in Research Preview, so behavior may change. The session-handoffs and CLAUDE.md patterns documented above remain the authoritative memory mechanism for self-hosted harnesses.

The Anti-Patterns

Reading entire files when you need 10 lines. A single 2,000-line file read consumes 15,000-20,000 tokens. Use line offsets: Read file.py offset=100 limit=20 saves the vast majority of that cost.¹⁵

Keeping verbose error output in context. After debugging a bug, your context holds 40+ stack traces from failed iterations. A single /compact after fixing the bug frees that dead weight.

Starting every session by reading every file. Let Claude Code’s glob and grep tools find relevant files on demand, saving 100,000+ tokens of unnecessary pre-loading.¹⁵

Subagent Patterns

Subagents are specialized Claude instances that handle complex tasks independently. They start with a clean context (no pollution from the main conversation), operate with specified tools, and return results as summaries. The exploration results do not bloat your main conversation; only the conclusions return.⁵

Built-In Subagent Types

Creating Custom Subagents

Define subagents in .claude/agents/ (project) or ~/.claude/agents/ (personal):

---
name: security-reviewer
description: Expert security code reviewer. Use PROACTIVELY after any code
  changes to authentication, authorization, or data handling.
tools: Read, Grep, Glob, Bash
model: opus
permissionMode: plan
---

You are a senior security engineer reviewing code for vulnerabilities.

When invoked:
1. Identify the files that were recently changed
2. Analyze for OWASP Top 10 vulnerabilities
3. Check for secrets, hardcoded credentials, SQL injection
4. Report findings with severity levels and remediation steps

Focus on actionable security findings, not style issues.

Subagent Configuration Fields

Worktree Isolation

Subagents can operate in temporary git worktrees, providing a complete isolated copy of the repository:⁵

---
name: experimental-refactor
description: Attempt risky refactoring in isolation
isolation: worktree
tools: Read, Write, Edit, Bash, Grep, Glob
---

You have an isolated copy of the repository. Make changes freely.
If the refactoring succeeds, the changes can be merged back.
If it fails, the worktree is discarded with no impact on the main branch.

Worktree isolation is essential for experimental work that might break the codebase.

Parallel Subagents

Use parallel subagents for independent research tasks that do not need to coordinate with each other:⁵

> Have three explore agents search in parallel:
> 1. Authentication code
> 2. Database models
> 3. API routes

Each agent runs in its own context window, finds relevant code, and returns a summary. The main context stays clean.

The Recursion Guard

Without spawn limits, agents delegate to agents that delegate to agents, each one losing context and burning tokens. The recursion guard pattern enforces budgets:¹⁶

#!/bin/bash
# recursion-guard.sh — enforce spawn budget
CONFIG_FILE="${HOME}/.claude/configs/recursion-limits.json"
STATE_FILE="${HOME}/.claude/state/recursion-depth.json"

MAX_DEPTH=2
MAX_CHILDREN=5
DELIB_SPAWN_BUDGET=2
DELIB_MAX_AGENTS=12

# Read current depth
current_depth=$(jq -r '.depth // 0' "$STATE_FILE" 2>/dev/null)

if [[ "$current_depth" -ge "$MAX_DEPTH" ]]; then
    echo "BLOCKED: Maximum recursion depth ($MAX_DEPTH) reached" >&2
    exit 2
fi

# Increment depth using safe arithmetic (not ((VAR++)) with set -e)
new_depth=$((current_depth + 1))
jq --argjson d "$new_depth" '.depth = $d' "$STATE_FILE" > "${STATE_FILE}.tmp"
mv "${STATE_FILE}.tmp" "$STATE_FILE"

Critical lesson: Use spawn budgets, not just depth limits. Depth-based limits track parent-child chains (blocked at depth 3) but miss width: 23 agents at depth 1 is still “depth 1.” A spawn budget tracks total active children per parent, capped at a configurable maximum. The budget model maps to the actual failure mode (too many total agents) rather than a proxy metric (too many nesting levels).⁷

Agent Teams (Research Preview)

Agent Teams coordinate multiple Claude Code instances that work independently, communicate via a shared mailbox and task list, and can challenge each other’s findings:⁵

Enable with: export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1

When to use agent teams vs subagents:

Multi-Agent Orchestration

Single-agent AI systems have a structural blind spot: they cannot challenge their own assumptions.⁷ Multi-agent deliberation forces independent evaluation from multiple perspectives before any decision locks.

Cross-tool orchestration (April 2026): Google open-sourced Scion on April 7 — a multi-agent hypervisor that runs Claude Code, Gemini CLI, and other “deep agents” as concurrent processes, each with isolated container, git worktree, and credentials. Runs local, hub, or Kubernetes. Explicit philosophy: “isolation over constraints” — agents run with high autonomy inside boundaries enforced at the infrastructure layer, not in the prompt.²⁵ This directly extends the subagent-isolation argument across different tool vendors. If your workflow spans Claude and OpenAI models, Scion is the first real reference implementation for cross-tool subagents with per-agent worktree + credential isolation.

Debate is not a silver bullet: The M3MAD-Bench research cluster (early 2026) found that multi-agent debate plateaus and can be subverted by misleading consensus — valid arguments lose when other agents confidently assert the wrong answer.²⁶ Tool-MAD improves this by giving each agent heterogeneous tool access and using Faithfulness/Relevance scores in the judge stage. If you’re building debate-style orchestration, invest in (a) tool heterogeneity per agent and (b) quantitative judge scoring rather than assuming more agents = better answers.

Managed Multiagent Orchestration and Outcomes (Public Beta)

If you don’t want to build the deliberation infrastructure described below, Multiagent Orchestration entered Public Beta in Claude Managed Agents on May 6, 2026.³⁵ Per Anthropic: “When there is too much work for a single agent to do well, multiagent orchestration lets a lead agent break the job into pieces and delegate each one to a specialist with its own model, prompt, and tools.”³⁵ Specialists “work in parallel on a shared filesystem and contribute to the lead agent’s overall context.”³⁵

Tracing comes in the box. Per Anthropic: “you can also trace every step in the Claude Console: which agent did what, in what order, and why, giving you full visibility into how your task was delegated and executed.”³⁵

The companion Public Beta feature is Outcomes. Per Anthropic: “you write a rubric describing what success looks like and the agent works toward it. A separate grader evaluates the output against your criteria in its own context window, so it isn’t influenced by the agent’s reasoning.”³⁵ This is the managed-service version of the two-gate validation pattern documented later in this section: the rubric replaces the hand-written gate, the separate grader replaces the consensus validator.

Self-hosted deliberation remains the right answer when the validation needs to integrate with your own hook surface (PreToolUse blocking, exit-code semantics, custom dispatchers) or when the harness must run without external dependencies. Managed Multiagent is the right answer when standard delegation plus rubric grading is the contract you actually need.

Minimum Viable Deliberation

Start with 2 agents and 1 rule: agents must evaluate independently before seeing each other’s work.⁷

Decision arrives
  |
  v
Confidence check: is this risky, ambiguous, or irreversible?
  |
  +-- NO  -> Single agent decides (normal flow)
  |
  +-- YES -> Spawn 2 agents with different system prompts
             Agent A: "Argue FOR this approach"
             Agent B: "Argue AGAINST this approach"
             |
             v
             Compare findings
             |
             +-- Agreement with different reasoning -> Proceed
             +-- Genuine disagreement -> Investigate the conflict
             +-- Agreement with same reasoning -> Suspect herding

This pattern covers 80% of the value. Everything else adds incremental improvement.

The Confidence Trigger

Not every task needs deliberation. A confidence scoring module evaluates four dimensions:¹⁷

1. Ambiguity - Does the query have multiple valid interpretations?
2. Domain complexity - Does it require specialized knowledge?
3. Stakes - Is the decision reversible?
4. Context dependency - Does it require understanding the broader system?

The score maps to three levels:

The threshold adapts by task type. Security decisions require 0.85 consensus. Documentation changes need only 0.50. This prevents over-engineering simple tasks while ensuring risky decisions get scrutiny.⁷

The State Machine

Seven phases, each gated by the previous:⁷

IDLE -> RESEARCH -> DELIBERATION -> RANKING -> PRD_GENERATION -> COMPLETE
                                                                    |
                                                              (or FAILED)

RESEARCH: Independent agents investigate the topic. Each agent gets a different persona (Technical Architect, Security Analyst, Performance Engineer, and others). Context isolation ensures agents cannot see each other’s findings during research.

DELIBERATION: Agents see all research findings and generate alternatives. The Debate agent identifies conflicts. The Synthesis agent combines non-contradictory findings.

RANKING: Each agent scores every proposed approach across 5 weighted dimensions:

The Two-Gate Validation Architecture

Two validation gates catch problems at different stages:⁷

Gate 1: Consensus Validation (PostToolUse hook). Runs immediately after each deliberation agent completes: 1. Phase must have reached at least RANKING 2. Minimum 2 agents completed (configurable) 3. Consensus score meets the task-adaptive threshold 4. If any agent dissented, concerns must be documented

Gate 2: Pride Check (Stop hook). Runs before the session can close: 1. Diverse methods: multiple unique personas represented 2. Contradiction transparency: dissents have documented reasons 3. Complexity handling: at least 2 alternatives generated 4. Consensus confidence: classified as strong (above 0.85) or moderate (0.70-0.84) 5. Improvement evidence: final confidence exceeds initial confidence

Two hooks at different lifecycle points match how failures actually occur: some are instant (bad score) and some are gradual (low diversity, missing dissent documentation).⁷

Why Agreement Is Dangerous

Charlan Nemeth studied minority dissent from 1986 through her 2018 book In Defense of Troublemakers. Groups with dissenters make better decisions than groups that reach quick agreement. The dissenter does not need to be right. The act of disagreement forces the majority to examine assumptions they would otherwise skip.¹⁸

Wu et al. tested whether LLM agents can genuinely debate and found that without structural incentives for disagreement, agents converge toward the most confident-sounding initial response regardless of correctness.¹⁹ Liang et al. identified the root cause as “Degeneration-of-Thought”: once an LLM establishes confidence in a position, self-reflection cannot generate novel counterarguments, making multi-agent evaluation structurally necessary.²⁰

Independence is the critical design constraint. Two agents evaluating the same deployment strategy with visibility into each other’s findings produced scores of 0.45 and 0.48. Same agents without visibility: 0.45 and 0.72. The gap between 0.48 and 0.72 is the cost of herding.⁷

Detecting Fake Agreement

A conformity detection module tracks patterns suggesting agents are agreeing without genuine evaluation:⁷

Score clustering: Every agent scoring within 0.3 points on a 10-point scale signals shared context contamination rather than independent assessment. When five agents evaluating an authentication refactor all scored security risk between 7.1 and 7.4, re-running with fresh context isolation spread the scores to 5.8-8.9.

Boilerplate dissent: Agents copying each other’s concern language rather than generating independent objections.

Absent minority perspectives: Unanimous approval from personas with conflicting priorities (a Security Analyst and a Performance Engineer rarely agree on everything).

The conformity detector catches the obvious cases (roughly 10-15% of deliberations where agents converge too quickly). For the remaining 85-90%, the consensus and pride check gates provide sufficient validation.

What Didn’t Work in Deliberation

Free-form debate rounds. Three rounds of back-and-forth text for a database indexing discussion produced 7,500 tokens of debate. Round 1: genuine disagreement. Round 2: restated positions. Round 3: identical arguments in different words. Structured dimension scoring replaced free-form debate, dropping cost by 60% while improving ranking quality.⁷

Single validation gate. The first implementation ran one validation hook at session end. An agent completed deliberation with a 0.52 consensus score (below threshold), then continued on unrelated tasks for 20 minutes before the session-end hook flagged the failure. Splitting into two gates (one at task completion, one at session end) caught the same problems at different lifecycle points.⁷

Cost of Deliberation

Each research agent processes roughly 5,000 tokens of context and generates 2,000-3,000 tokens of findings. With 3 agents, that is 15,000-24,000 additional tokens per decision. With 10 agents, roughly 50,000-80,000 tokens.⁷

At current Opus pricing, a 3-agent deliberation costs approximately $0.68-0.90. A 10-agent deliberation costs $2.25-3.00. The system triggers deliberation on roughly 10% of decisions, so the amortized cost across all decisions is $0.23-0.30 per session. Whether that is worth it depends on what a bad decision costs.

When to Deliberate

CLAUDE.md Design

CLAUDE.md is operational policy for an AI agent, not a README for humans.²¹ The agent does not need to understand why you use conventional commits. It needs to know the exact command to run and what “done” looks like.

The Precedence Hierarchy

Rules files load automatically and provide structured context without cluttering CLAUDE.md.⁶

What Gets Ignored

These patterns reliably produce no observable change in agent behavior:²¹

Prose paragraphs without commands. “We value clean, well-tested code” is documentation, not operations. The agent reads it and proceeds to write code without tests because there is no actionable instruction.

Ambiguous directives. “Be careful with database migrations” is not a constraint. “Run alembic check before applying migrations. Abort if downgrade path is missing.” is.

Contradictory priorities. “Move fast and ship quickly” plus “Ensure comprehensive test coverage” plus “Keep runtime under 5 minutes” plus “Run full integration tests before every commit.” The agent cannot satisfy all four simultaneously and defaults to skipping verification.²¹

Style guides without enforcement. “Follow the Google Python Style Guide” without ruff check --select D gives the agent no mechanism to verify compliance.

What Works

Command-first instructions:

## Build and Test Commands
- Install: `pip install -r requirements.txt`
- Lint: `ruff check . --fix`
- Format: `ruff format .`
- Test: `pytest -v --tb=short`
- Type check: `mypy app/ --strict`
- Full verify: `ruff check . && ruff format --check . && pytest -v`

Closure definitions:

## Definition of Done
A task is complete when ALL of the following pass:
1. `ruff check .` exits 0
2. `pytest -v` exits 0 with no failures
3. `mypy app/ --strict` exits 0
4. Changed files have been staged and committed
5. Commit message follows conventional format: `type(scope): description`

Task-organized sections:

## When Writing Code
- Run `ruff check .` after every file change
- Add type hints to all new functions

## When Reviewing Code
- Check for security issues: `bandit -r app/`
- Verify test coverage: `pytest --cov=app --cov-fail-under=80`

## When Releasing
- Update version in `pyproject.toml`
- Run full suite: `pytest -v && ruff check . && mypy app/`

Escalation rules:

## When Blocked
- If tests fail after 3 attempts: stop and report the failing test with full output
- If a dependency is missing: check `requirements.txt` first, then ask
- Never: delete files to resolve errors, force push, or skip tests

Writing Order

If starting from scratch, add sections in this priority order:²¹

1. Build and test commands (the agent needs these before it can do anything useful)
2. Definition of done (prevents false completions)
3. Escalation rules (prevents destructive workarounds)
4. Task-organized sections (reduces irrelevant instruction parsing)
5. Directory scoping (monorepos: keeps service instructions isolated)

Skip style preferences until the first four are working.

File Imports

Reference other files within CLAUDE.md:

See @README.md for project overview
Coding standards: @docs/STYLE_GUIDE.md
API documentation: @docs/API.md
Personal preferences: @~/.claude/preferences.md

Import syntax: relative (@docs/file.md), absolute (@/absolute/path.md), or home directory (@~/.claude/file.md). Maximum depth: 5 levels of imports.⁶

Cross-Tool Instruction Compatibility

AGENTS.md is an open standard recognized by every major AI coding tool.²¹ If your team uses multiple tools, write AGENTS.md as the canonical source and mirror relevant sections to tool-specific files:

The patterns in AGENTS.md (command-first, closure-defined, task-organized) work in any instruction file regardless of tool. Do not maintain parallel instruction sets that drift apart. Write one authoritative source and mirror.

Codex Parity Notes

Codex now has first-class equivalents for the major harness layers, but the migration is a pattern translation, not a file copy. Codex reads AGENTS.md before work begins, layering global guidance from ~/.codex with project and nested repository instructions.³¹ Codex skills use the same SKILL.md mental model with progressive disclosure: Codex starts with the skill name, description, and file path, then loads the full skill only when it decides to use it.³² Codex also has native hooks, plugin-bundled hooks, managed hooks, MCP support, and explicit subagent workflows.³³³⁴

The practical mapping:

The important difference: Claude subagents can be selected automatically from descriptions, while Codex currently documents subagent workflows as explicit. That makes skills and hooks the right default for always-on harness behavior in Codex; subagents are for deliberate parallel work, review, and exploration.

Testing Your Instructions

Verify the agent actually reads and follows your instructions:

# Check active instructions
claude --print "What instructions are you following for this project?"

# Verify specific rules are active
claude --print "What is your definition of done?"

The acid test: Ask the agent to explain your build commands. If it cannot reproduce them verbatim, the instructions are either too verbose (content pushed out of context), too vague (agent cannot extract actionable instructions), or not being discovered. GitHub’s analysis of 2,500 repositories found that vagueness causes most failures.²¹

Production Patterns

Opus 4.7 Long-Horizon Patterns (April 2026)

Claude Opus 4.7 (April 16, 2026) shipped with specific capabilities that change what a harness needs to defend against:²⁹

• Tool-failure resilience: Opus 4.7 continues through tool failures that halted Opus 4.6 sessions. You can reduce — but not eliminate — defensive retry wrappers in subagent code. Keep the hook-level guards; trim the in-prompt “if the tool fails, try again three times” scaffolding.
• xhigh effort tier (Opus-4.7 only): Sits between high and max. Recommended default for coding and agentic workloads. On long-running subagents, xhigh meaningfully outperforms high with sub-proportional token cost. max remains the right choice for single-shot hard reasoning; xhigh is better for sustained tasks.
• Token-budget ceiling: Configurable per agent run via output_config.task_budget (beta header task-budgets-2026-03-13). The model sees a running countdown and gracefully scopes work to the budget instead of running out unexpectedly. Use for agentic loops where you want predictable token spend without sacrificing quality on short prompts.
• Implicit-need awareness: First Claude model to pass “implicit-need” tests — recognizing when the user’s literal request underspecifies what they actually need. This makes CLAUDE.md’s “clarifying rules” section less necessary. If your CLAUDE.md is 200 lines of “also consider X when the user asks for Y” guardrails, prune the ones that are now covered natively.

Worktree Base, Sandbox Paths, and Admin Settings (May 7, 2026)

Claude Code v2.1.133 adds four admin-tier settings worth knowing about for production harnesses:³⁹

The worktree.baseRef revert is the one to flag for users: agents that relied on the v2.1.128-v2.1.132 behavior (worktrees branching from local HEAD) lose access to unpushed work in fresh worktrees unless they opt back in.

The Quality Loop

A mandatory review process for all non-trivial changes:

1. Implement - Write the code
2. Review - Re-read every line. Catch typos, logic errors, unclear sections
3. Evaluate - Run the evidence gate. Check patterns, edge cases, test coverage
4. Refine - Fix every issue. Never defer to “later”
5. Zoom Out - Check integration points, imports, adjacent code for regressions
6. Repeat - If any evidence gate criterion fails, return to step 4
7. Report - List what changed, how verified, cite specific evidence

The Evidence Gate

“I believe” and “it should” are not evidence. Cite file paths, test output, or specific code.

If you cannot produce evidence for any row, return to Refine.²²

Error Handling Patterns

Atomic file writes. Multiple agents writing to the same state file simultaneously corrupts JSON. Write to .tmp files, then mv atomically. The OS guarantees mv is atomic on the same filesystem.¹⁷

# Atomic state update
jq --argjson d "$new_depth" '.depth = $d' "$STATE_FILE" > "${STATE_FILE}.tmp"
mv "${STATE_FILE}.tmp" "$STATE_FILE"

State corruption recovery. If state gets corrupted, the recovery pattern recreates from safe defaults rather than crashing:¹⁶

if ! jq -e '.depth' "$RECURSION_STATE_FILE" &>/dev/null; then
    # Corrupted state file, recreate with safe defaults
    echo '{"depth": 0, "agent_id": "root", "parent_id": null}' > "$RECURSION_STATE_FILE"
    echo "- Recursion state recovered (was corrupted)"
fi

The ((VAR++)) bash trap. ((VAR++)) returns exit code 1 when VAR is 0 because 0++ evaluates to 0, which bash treats as false. With set -e enabled, this kills the script. Use VAR=$((VAR + 1)) instead.¹⁶

Blast Radius Classification

Classify every agent action by blast radius and gate accordingly:²

Remote Control (connecting to local Claude Code from any browser or mobile app) turns the “External” gate from a blocking wait into an async notification. The agent keeps working on the next task while you review the previous one from your phone.²

Task Specification for Autonomous Runs

Effective autonomous tasks include three elements: objective, completion criteria, and context pointers:¹⁶

OBJECTIVE: Implement multi-agent deliberation with consensus validation.

COMPLETION CRITERIA:
- All tests in tests/test_deliberation_lib.py pass (81 tests)
- post-deliberation.sh validates consensus above 70% threshold
- recursion-guard.sh enforces spawn budget (max 12 agents)
- No Python type errors (mypy clean)

CONTEXT:
- Follow patterns in lib/deliberation/state_machine.py
- Consensus thresholds in configs/deliberation-config.json
- Spawn budget model: agents inherit budget, not increment depth

Criteria must be machine-verifiable: test pass/fail, linter output, HTTP status codes, file existence checks. An early task that asked the agent to “write tests that pass” produced assert True and assert 1 == 1. Technically correct. Practically worthless.¹⁶

Failure Modes to Watch For

A Concrete Session Trace

A session trace from an autonomous run processing a PRD with 5 stories:²

1. SessionStart fires. Dispatcher injects: current date, project detection, philosophy constraints, cost tracking initialization. Five hooks, 180ms total.

2. Agent reads the PRD, plans the first story. UserPromptSubmit fires. Dispatcher injects: active project context, session drift baseline.

3. Agent calls Bash to run tests. PreToolUse:Bash fires. Credentials check, sandbox validation, project detection. 90ms. Tests run. PostToolUse:Bash fires: activity heartbeat logged, drift check.

4. Agent calls Write to create a file. PreToolUse:Write fires: file scope check. PostToolUse:Write fires: lint check, commit tracking.

5. Agent finishes the story. Stop fires. Quality gate checks: did the agent cite evidence? Hedging language? TODO comments in the diff? If any check fails, exit 2 and the agent continues.

6. Independent verification: A fresh agent runs the test suite without trusting the previous agent’s self-report.

7. Three code review agents spawn in parallel. Each reviews the diff independently. If any reviewer flags CRITICAL, the story goes back in the queue.

8. Story passes. Next story loads. The cycle repeats for all 5 stories.

Total hooks fired across 5 stories: ~340. Total time in hooks: ~12 seconds. That overhead prevented three credential leaks, one destructive command, and two incomplete implementations in a single overnight run.

Case Study: Overnight PRD Processing

A production harness processed 12 PRDs (47 stories) across 8 overnight sessions. Metrics compare the first 4 PRDs (minimal harness: CLAUDE.md only) against the last 8 (full harness: hooks, skills, quality gates, multi-agent review).

The two credential leaks required rotating API keys and auditing downstream services: roughly 4 hours of incident response. The harness overhead that prevented the equivalent was 2.4 seconds of bash per story. The false completion rate dropped from 35% to 4% because the Stop hook independently ran tests before allowing the agent to report done.

Security Considerations

The Five Principles of Trustworthy Agents (Anthropic, April 2026)

Anthropic published a formal framework for agent trustworthiness on April 9, 2026.²⁷ The five principles parallel — and extend — the Evidence Gate thinking in this guide:

Anthropic also donated MCP to the Linux Foundation’s Agentic AI Foundation, joining AGENTS.md (now jointly stewarded with OpenAI, Google, Cursor, Factory, Sourcegraph). Agent interoperability standards are now vendor-neutral.²⁷

Skill sandbox tooling: For teams that treat skills as an attack surface, Permiso’s SandyClaw (launched April 2, 2026) runs skills in a dedicated sandbox and delivers evidence-backed verdicts from Sigma/YARA/Nova/Snort detection. First product in the skill-sandbox category.²⁸

The Sandbox

Claude Code supports an optional sandbox mode (enabled via settings.json or the /sandbox command) that restricts network access and filesystem operations using OS-level isolation (seatbelt on macOS, bubblewrap on Linux). When enabled, the sandbox prevents the model from making arbitrary network requests or accessing files outside the project directory. Without sandboxing, Claude Code uses a permission-based model where you approve or deny individual tool calls.¹³

Permission Boundaries

The permission system gates operations at multiple levels:

Prompt Injection Defense

Skills and hooks provide defense-in-depth against prompt injection:

Skills with tool restrictions prevent a compromised prompt from gaining write access:

allowed-tools: Read, Grep, Glob

PreToolUse hooks validate every tool call regardless of how the model was prompted:

# Block credential file access regardless of prompt
if echo "$FILE_PATH" | grep -qE "\.(env|pem|key|credentials)$"; then
    echo "BLOCKED: Sensitive file access" >&2
    exit 2
fi

Subagent isolation limits blast radius. A subagent with permissionMode: plan cannot make changes even if its prompt is compromised.

Hook Security

HTTP hooks that interpolate environment variables into headers require an explicit allowedEnvVars list to prevent arbitrary environment variable exfiltration:¹³

{
  "type": "http",
  "url": "https://api.example.com/notify",
  "headers": {
    "Authorization": "Bearer $MY_TOKEN"
  },
  "allowedEnvVars": ["MY_TOKEN"]
}

The Human-Agent Division of Responsibility

Security in agent architectures requires a clear division between human and agent responsibilities:¹⁷

The pattern: humans own decisions that require organizational context, ethical judgment, or strategic direction. Agents own decisions that require computational search across large possibility spaces. Hooks enforce the boundary.

Recursive Hook Enforcement

Hooks fire for subagent actions too.¹³ If Claude spawns a subagent via the Agent tool, your PreToolUse and PostToolUse hooks execute for every tool the subagent uses. Without recursive hook enforcement, a subagent could bypass your safety gates. The SubagentStop event lets you run cleanup or validation when a subagent completes.

This is not optional. An agent that spawns a subagent without your security hooks is an agent that can force-push to main, read credential files, or run destructive commands while your gates watch the main conversation do nothing.

Cost as Architecture

Cost is an architectural decision, not an operational afterthought.² Three levels:

Token level. System prompt compression. Remove tutorial code examples (the model knows the APIs), collapse duplicate rules across files, and replace explanations with constraints. “Reject tool calls matching sensitive paths” does the same work as a 15-line explanation of why credentials should not be read.

Agent level. Fresh spawns over long conversations. Each story in an autonomous run gets a new agent with a clean context. The context never balloons because each agent starts fresh. Briefing instead of memory: models execute a clear briefing better than they navigate 30 steps of accumulated context.

Architecture level. CLI-first over MCP when the operation is stateless. A claude --print call for a one-shot evaluation costs less and adds no connection overhead. MCP makes sense when the tool needs persistent state or streaming.

Decision Framework

When to use each mechanism:

Skills vs Hooks vs Subagents

FAQ

How many hooks is too many?

Performance, not count, is the constraint. Each hook runs synchronously, so total hook execution time adds to every matched tool call. 95 hooks across user-level and project-level settings run without noticeable latency when each hook completes in under 200ms. The threshold to watch: if a PostToolUse hook adds more than 500ms to every file edit, the session feels sluggish. Profile your hooks with time before deploying them.¹⁴

Can hooks block Claude Code from running a command?

Yes. PreToolUse hooks block any tool action by exiting with code 2. Claude Code cancels the pending action and shows the hook’s stderr output to the model. Claude sees the rejection reason and suggests a safer alternative. Exit 1 is a non-blocking warning where the action still proceeds.³

Where should I put hook configuration files?

Hook configurations go in .claude/settings.json for project-level hooks (committed to your repository, shared with your team) or ~/.claude/settings.json for user-level hooks (personal, applied to every project). Project-level hooks take precedence when both exist. Use absolute paths for script files to avoid working-directory issues.¹⁴

Does every decision need deliberation?

No. The confidence module scores decisions across four dimensions (ambiguity, complexity, stakes, context dependency). Only decisions scoring below 0.70 overall confidence trigger deliberation, roughly 10% of total decisions. Documentation fixes, variable renames, and routine edits skip deliberation entirely. Security architecture, database schema changes, and irreversible deployments trigger it consistently.⁷

How do I test a system designed to produce disagreement?

Test both success paths and failure paths. Success: agents disagree productively and reach consensus. Failure: agents converge too quickly, never converge, or exceed spawn budgets. End-to-end tests simulate each scenario with deterministic agent responses, verifying that both validation gates catch every documented failure mode. A production deliberation system runs 141 tests across three layers: 48 bash integration tests, 81 Python unit tests, and 12 end-to-end pipeline simulations.⁷

What is the latency impact of deliberation?

A 3-agent deliberation adds 30-60 seconds of wall-clock time (agents run sequentially through the Agent tool). A 10-agent deliberation adds 2-4 minutes. The consensus and pride check hooks each run in under 200ms. The primary bottleneck is LLM inference time per agent, not orchestration overhead.⁷

How long should a CLAUDE.md file be?

Keep each section under 50 lines and the total file under 150 lines. Long files get truncated by context windows, so front-load the most critical instructions: commands and closure definitions before style preferences.²¹

Can this work with tools other than Claude Code?

The architectural principles (hooks as deterministic gates, skills as domain expertise, subagents as isolated contexts, filesystem as memory) apply conceptually to any agentic system. The specific implementation uses Claude Code’s lifecycle events, matcher patterns, and Agent tool. AGENTS.md carries the same patterns to Codex, Cursor, Copilot, Amp, and Windsurf.²¹ The harness pattern is tool-agnostic even if the implementation details are tool-specific.

Quick Reference Card

Hook Configuration

{
  "hooks": {
    "PreToolUse": [{"matcher": "Bash", "hooks": [{"type": "command", "command": "script.sh"}]}],
    "PostToolUse": [{"matcher": "Write|Edit", "hooks": [{"type": "command", "command": "format.sh"}]}],
    "Stop": [{"matcher": "", "hooks": [{"type": "agent", "prompt": "Verify tests pass. $ARGUMENTS"}]}],
    "SessionStart": [{"matcher": "", "hooks": [{"type": "command", "command": "setup.sh"}]}]
  }
}

Skill Frontmatter

---
name: my-skill
description: What it does and when to use it. Include trigger phrases.
allowed-tools: Read, Grep, Glob
---

Subagent Definition

---
name: my-agent
description: When to invoke. Include PROACTIVELY for auto-delegation.
tools: Read, Grep, Glob, Bash
model: opus
permissionMode: plan
---

Instructions for the subagent.

Exit Codes

Key Commands

File Locations

Changelog

References