Agentic Design Patterns — Claude Code Expert

Comprehensive pattern catalog integrating Andrew Ng's four foundational patterns, Anthropic's workflow/agent taxonomy, and the extended patterns from Gulli & Sauco (2025). Each pattern includes a concrete Claude Code implementation template mapped to the 4-layer extension stack.

Two Taxonomies, One Stack

Andrew Ng's Four Foundational Patterns

Reflection, Tool Use, Planning, Multi-Agent — the building blocks that kicked off the field.

Anthropic's Workflow vs Agent Distinction

Anthropic draws a critical line between Workflows (developer-controlled, deterministic paths) and Agents (LLM-directed, autonomous processes). Most production systems are workflows, not agents. This distinction prevents over-engineering.

Type	Control	When to Use	Claude Code Example
Workflow	Developer-defined path	Task is decomposable, steps are known	Prompt chain, routing, parallelization
Agent	LLM chooses next action	Task is open-ended, steps emerge at runtime	cc-intel deep analysis, cc-troubleshoot

Rule of thumb: Start with the simplest workflow pattern that solves your problem. Escalate to agents only when the task genuinely requires dynamic decision-making.

---

Part 1: Workflow Patterns (Deterministic)

1. Prompt Chaining

Sequential LLM calls where each output feeds the next. The simplest multi-step pattern.

When to use: Task has 2-5 distinct phases with clear input/output contracts. Achieves: Up to 15.6% better accuracy than monolithic prompts (Gulli & Sauco, 2025).

4-Layer Implementation:

Layer 1 (CLAUDE.md):
  "For multi-step analysis, use the chain: fingerprint → analyze → plan → implement"

Layer 2 (Skill):
  skills/deep-code-intelligence/SKILL.md defines the 4-step chain with
  explicit output schemas per step

Layer 3 (Hook):
  PostToolUse hook validates each chain step's output before the next begins
  hooks/chain-validator.sh checks JSON schema compliance

Layer 4 (Agent):
  agents/principal-engineer-strategist.md executes the chain with
  typed handoff objects between steps

Template — Chain with Typed Handoffs:

chain:
  - step: fingerprint
    input: { repoPath: string }
    output: { languages: string[], frameworks: string[], services: string[] }
    agent: haiku  # cheap discovery
  - step: analyze
    input: $fingerprint.output
    output: { gaps: Gap[], risks: Risk[], strengths: string[] }
    agent: sonnet  # moderate reasoning
  - step: plan
    input: $analyze.output
    output: { steps: Step[], dependencies: DAG, estimatedCost: number }
    agent: opus  # deep planning
  - step: implement
    input: $plan.output
    output: { files: FileChange[], tests: string[], commitMessage: string }
    agent: sonnet  # execution

2. Routing

Classify input and direct to specialized handler. Prevents wasting expensive models on trivial tasks.

When to use: Requests vary widely in complexity or domain.

4-Layer Implementation:

Layer 1 (CLAUDE.md):
  "Route requests by complexity: trivial→haiku, standard→sonnet, architecture→opus"

Layer 2 (Skill):
  skills/model-routing/SKILL.md — decision matrix with cost/capability tables

Layer 3 (Hook):
  PreToolUse hook on Agent tool checks complexity signal before spawning

Layer 4 (Agent):
  Routing is a function, not an agent. The orchestrator applies the routing
  schema before delegating.

Template — Intent Router:

router:
  classify:
    prompt: |
      Classify this request:
      - lookup: factual question, needs retrieval only
      - implement: code change with clear spec
      - analyze: needs deep reasoning or tradeoff analysis
      - debug: error diagnosis and fix
      - orchestrate: requires multiple agents
    model: haiku  # classification is cheap
  routes:
    lookup: { model: haiku, tools: [Read, Grep, Glob] }
    implement: { model: sonnet, tools: [Read, Write, Edit, Bash] }
    analyze: { model: opus, tools: [Read, Grep, Glob, Agent] }
    debug: { model: sonnet, tools: [Read, Grep, Bash, Agent] }
    orchestrate: { model: opus, tools: [Agent], pattern: orchestrator-workers }

3. Parallelization

Execute independent subtasks simultaneously, then merge results.

When to use: Subtasks are independent (no data dependency between them). Two variants:

• Sectioning: Split work by domain (security agent + performance agent + style agent)
• Voting: Same task, multiple agents, majority wins (reduces variance)

4-Layer Implementation:

Layer 1 (CLAUDE.md):
  "For code review, run security/performance/style agents in parallel"

Layer 2 (Skill):
  skills/council-review/SKILL.md — fan-out templates with merge protocols

Layer 3 (Hook):
  No hook needed — parallelism is orchestrator-managed

Layer 4 (Agent):
  agents/council-coordinator.md fans out to N agents, collects results,
  runs structured aggregation (not ad-hoc concatenation)

Template — Parallel Review with Structured Merge:

parallel:
  fan_out:
    - agent: security-reviewer
      model: sonnet
      prompt: "Review for OWASP Top 10, secrets exposure, injection risks"
      output: { findings: Finding[], severity: 'critical'|'high'|'medium'|'low' }
    - agent: performance-reviewer
      model: sonnet
      prompt: "Review for N+1 queries, memory leaks, bundle size, algorithmic complexity"
      output: { findings: Finding[], impact: 'blocking'|'degraded'|'minor' }
    - agent: style-reviewer
      model: haiku
      prompt: "Review for naming conventions, dead code, consistency with existing patterns"
      output: { findings: Finding[], category: 'style'|'consistency'|'cleanup' }
  merge:
    strategy: structured_aggregation  # not concatenation
    dedup: true  # remove duplicate findings across agents
    rank_by: severity_then_confidence
    output: { findings: Finding[], summary: string, score: 0-100 }

4. Evaluator-Optimizer Loop

A generator produces output; an evaluator scores it; the generator refines based on feedback. Repeat until quality threshold is met or max iterations reached.

When to use: Output quality matters more than speed. Code generation, configuration authoring, prompt engineering.

4-Layer Implementation:

Layer 1 (CLAUDE.md):
  "Generated configs must pass the evaluator with score >= 80 before committing"

Layer 2 (Skill):
  skills/agentic-patterns/SKILL.md (this file) — loop template

Layer 3 (Hook):
  PreToolUse hook on Write/Edit can trigger evaluation before file save

Layer 4 (Agent):
  agents/evaluator-optimizer.md — runs the generate→evaluate→refine cycle

Template — Eval-Optimize Loop:

evaluator_optimizer:
  max_iterations: 3
  quality_threshold: 80  # 0-100 score
  generator:
    model: sonnet
    prompt: "Generate {artifact} meeting these requirements: {spec}"
    output: { artifact: string, reasoning: string }
  evaluator:
    model: opus  # evaluator should be at least as capable as generator
    rubric:
      - correctness: "Does the output meet all requirements?"
      - completeness: "Are edge cases handled?"
      - style: "Does it follow project conventions?"
      - safety: "Are there security concerns?"
    output: { score: 0-100, pass: boolean, critique: string[], suggestions: string[] }
  refine:
    input: { original: $generator.output, critique: $evaluator.output }
    prompt: "Revise the artifact addressing each critique point. Do not regress on passing criteria."
  on_pass: commit
  on_max_iterations: present_best_with_warnings

5. Orchestrator-Workers

A central orchestrator dynamically breaks work into subtasks and delegates to workers. Unlike parallelization, the orchestrator decides what to delegate at runtime.

When to use: Task decomposition isn't known upfront — the orchestrator must reason about what workers are needed.

4-Layer Implementation:

Layer 1 (CLAUDE.md):
  "For complex features, use orchestrator-workers pattern via /cc-orchestrate"

Layer 2 (Skill):
  skills/agent-teams/SKILL.md — team topology patterns

Layer 3 (Hook):
  PostToolUse hook on Agent tracks worker results for the orchestrator

Layer 4 (Agent):
  agents/team-orchestrator.md — dynamic delegation with result synthesis

Template — Dynamic Orchestrator:

orchestrator_workers:
  orchestrator:
    model: opus
    system: |
      You are a lead engineer. Analyze the task, decompose it into subtasks,
      and delegate each to the most appropriate worker. After all workers report,
      synthesize a final result. You may spawn additional workers if gaps emerge.
    tools: [Agent, Read, Grep, Glob, TodoWrite]
  workers:
    available:
      - name: implementer
        model: sonnet
        specialty: "Write code changes"
      - name: tester
        model: sonnet
        specialty: "Write and run tests"
      - name: reviewer
        model: sonnet
        specialty: "Review code for bugs and style"
      - name: researcher
        model: haiku
        specialty: "Look up docs, search codebase, find examples"
    spawn_policy: on_demand  # orchestrator decides which workers to use
  coordination:
    shared_state: todo_list  # workers update shared TodoWrite
    max_workers: 5
    timeout_per_worker: 120s

---

Part 2: Agent Patterns (LLM-Directed)

6. Reflection (Self-Critique)

Agent reviews its own output before presenting it. The simplest agent pattern.

When to use: Any code generation or analysis where a second look catches errors.

4-Layer Implementation:

Layer 3 (Hook):
  PostToolUse hook on Write/Edit triggers a self-review pass

Layer 4 (Agent):
  agents/audit-reviewer.md — second-round review agent

Template — Self-Review Loop:

reflection:
  generate:
    prompt: "Solve this problem: {task}"
  self_review:
    prompt: |
      Review your solution. Check for:
      1. Off-by-one errors
      2. Unhandled edge cases (null, empty, boundary values)
      3. Missing error handling at system boundaries
      4. Inconsistency with existing code patterns
      Return: { issues: string[], revised: string | null }
  policy:
    if_issues_found: revise_and_present  # auto-fix before showing user
    max_revisions: 2

7. ReAct Planning (Thought → Action → Observation)

Agent explicitly reasons before acting, observes the result, then reasons again.

When to use: Debugging, investigation, any task where the next step depends on what you learn.

Template — ReAct Cycle:

react:
  system: |
    For each step, follow this cycle:
    THOUGHT: What do I know? What do I need to find out? What's my hypothesis?
    ACTION: Execute one tool call to test the hypothesis.
    OBSERVATION: What did I learn? Does it confirm or refute my hypothesis?
    Repeat until the task is complete or you need human input.
  trace_log: .claude/traces/{task_id}.jsonl  # auditable reasoning trail
  max_cycles: 20
  escalation: ask_human  # if stuck after max_cycles

8. Multi-Agent with Shared Blackboard

Multiple specialized agents work on a shared problem, reading and writing to a common state store (the "blackboard"). Agents can see each other's findings in real-time.

When to use: Cross-domain analysis where findings in one area inform another (e.g., security finding reveals performance issue).

Template — Blackboard Protocol:

blackboard:
  store: .claude/blackboard/{session_id}.json
  schema:
    findings: Finding[]
    open_questions: Question[]
    conflicts: Conflict[]
    decisions: Decision[]
  agents:
    - role: security-analyst
      reads: [findings, open_questions]
      writes: [findings, open_questions, conflicts]
    - role: performance-analyst
      reads: [findings, open_questions]
      writes: [findings, open_questions]
    - role: architect
      reads: [findings, conflicts, open_questions]
      writes: [decisions, findings]
  protocol:
    1. Each agent reads blackboard before starting
    2. Agents write findings as they discover them
    3. If agent A's finding conflicts with agent B's, write to conflicts[]
    4. Architect resolves conflicts and writes decisions[]
    5. All agents re-read decisions before finalizing
  merge: architect_synthesizes

---

Part 3: Supporting Patterns

9. Guardrails (Pre/Post Execution)

Hard limits on what agents can do. Implemented as hooks, not agents.

4-Layer Implementation:

Layer 3 (Hook — this is the primary layer):
  PreToolUse on Bash:
    - Block destructive commands (rm -rf, DROP TABLE, git push --force)
    - Block secret exposure (echo $API_KEY)
    - Enforce path scoping (no writes outside project root)
  PreToolUse on Write/Edit:
    - Block writes to .env, credentials.json, *.pem
    - Enforce file size limits
  PostToolUse on Bash:
    - Scan output for leaked secrets

10. Human-in-the-Loop (HITL) Checkpoints

Pause execution at defined points for human approval.

Template — HITL Gates:

hitl:
  gates:
    - trigger: before_commit
      summary: "Show diff summary, ask for approval"
      timeout: 300s  # auto-cancel if no response
    - trigger: before_deploy
      summary: "Show deployment plan, target environment, rollback strategy"
      requires: explicit_yes
    - trigger: on_conflict
      summary: "Multiple agents disagree — present options to human"
      present: { options: ConflictResolution[], recommendation: string }
  resume: from_checkpoint  # don't restart, resume where paused

11. Exception Handling & Recovery

Structured error classification and recovery strategies.

Template — Error Recovery:

exception_handling:
  classify:
    transient: [timeout, rate_limit, network_error]
    permanent: [file_not_found, permission_denied, invalid_syntax]
    ambiguous: [exit_code_1, empty_output]
  strategies:
    transient: retry_with_backoff(max=3, base=2s)
    permanent: escalate_to_human
    ambiguous: try_alternative_approach
  fallback_chain:
    - primary: Bash("npm test")
    - fallback_1: Bash("npx jest --no-coverage")
    - fallback_2: Bash("node --test")
    - final: report_failure_with_context

12. Memory & Feedback Loops

Cross-session learning through structured memory persistence.

4-Layer Implementation:

Layer 1 (CLAUDE.md):
  rules/lessons-learned.md — auto-growing error/fix knowledge base

Layer 3 (Hook):
  PostToolUseFailure → captures error to lessons-learned.md
  SessionStart → loads lessons-learned.md as context

Layer 2 (Skill):
  skills/self-healing-advanced/SKILL.md — pattern detection, rule promotion
  skills/memory-instructions/SKILL.md — 3-tier memory architecture

Feedback Loop:

Agent produces output
    → Evaluator scores output
    → Score logged to .claude/eval-log/{date}.jsonl
    → Weekly: aggregation detects patterns (3+ similar low scores)
    → Pattern promoted to .claude/rules/ as permanent rule
    → Future sessions apply rule automatically

13. Resource-Aware Optimization

Dynamic model/agent selection based on remaining budget.

Template — Budget-Aware Routing:

resource_aware:
  budget:
    max_tokens: 500000
    max_cost: $2.00
  checkpoints:
    - at: 25%_remaining
      action: downgrade_all_agents_to_haiku
    - at: 10%_remaining
      action: complete_current_task_only
    - at: 5%_remaining
      action: save_state_and_stop
  dynamic_routing:
    if budget > 75%: use_optimal_model_per_task
    if budget 25-75%: prefer_sonnet_over_opus
    if budget < 25%: haiku_only

---

Part 4: Pattern Selection Decision Tree

Is the task decomposable into known steps?
├── YES → Is it sequential?
│   ├── YES → Prompt Chaining (#1)
│   └── NO → Are subtasks independent?
│       ├── YES → Parallelization (#3)
│       └── NO → Orchestrator-Workers (#5)
├── PARTIALLY → Do you know the first step but not the rest?
│   └── YES → ReAct Planning (#7)
└── NO → Is quality critical?
    ├── YES → Evaluator-Optimizer Loop (#4)
    └── NO → Single agent with Reflection (#6)

Do multiple domains need to coordinate?
├── YES → Multi-Agent with Blackboard (#8)
└── NO → Single agent or simple chain

Does input vary widely in type/complexity?
├── YES → Routing (#2)
└── NO → Direct to appropriate pattern

Part 5: Anti-Patterns (What NOT to Do)

Anti-Pattern	Why It Fails	Fix
Agent for everything	25x cost increase, token waste	Start with simplest workflow, escalate only when needed
No evaluation	Garbage in, garbage out across chain	Add evaluator at every handoff point
Concatenation as merge	Duplicate findings, no ranking	Use structured aggregation with dedup
Same model everywhere	Overpaying for simple tasks	Route: haiku for lookup, sonnet for impl, opus for arch
Infinite loops	Agent retries forever on permanent errors	Max iterations + error classification
Monolithic agent	Context overflow on complex tasks	Decompose into chain or orchestrator-workers
No HITL gates	Destructive action without approval	Add gates before commits, deploys, destructive ops

Quick Reference: Pattern → 4-Layer Mapping

Pattern	L1 CLAUDE.md	L2 Skill	L3 Hook	L4 Agent
Prompt Chaining	Chain order	deep-code-intelligence	chain-validator	principal-engineer-strategist
Routing	Complexity rules	model-routing	PreToolUse check	(function, not agent)
Parallelization	Review protocol	council-review	—	council-coordinator
Eval-Optimizer	Quality threshold	(this skill)	PreToolUse on Write	evaluator-optimizer
Orchestrator-Workers	Delegation rules	agent-teams	PostToolUse tracking	team-orchestrator
Reflection	—	self-healing-advanced	PostToolUse review	audit-reviewer
ReAct Planning	—	deep-code-intelligence	—	principal-engineer-strategist
Blackboard	—	council-review	—	council-coordinator
Guardrails	Forbidden ops	enterprise-security	Pre/PostToolUse	—
HITL	Approval gates	—	PreToolUse prompt	—
Exception Handling	—	self-healing-advanced	PostToolUseFailure	claude-code-debugger
Memory/Feedback	lessons-learned	memory-instructions	SessionStart/Failure	—
Resource-Aware	Budget rules	cost-optimization	—	—

References

• Ng, A. (2024). "Agentic Design Patterns" — four foundational patterns.
• Anthropic (2024). "Building Effective Agents" — workflow vs agent taxonomy.
• Gulli, A. & Sauco, M. (2025). Agentic Design Patterns. O'Reilly Media.
• Repository: github.com/Mathews-Tom/Agentic-Design-Patterns
• Related: skills/model-routing/SKILL.md, skills/council-review/SKILL.md
• Related: skills/agent-teams/SKILL.md, skills/deep-code-intelligence/SKILL.md