agentic-patterns · v5.0.0 · 2026-09-15 · sha256 5709674b817ba5da
agentic-patterns v5.0.0A
Immutable. This exact content never changes and is served at /api/v1/blob/5709674b817ba5da.
---
name: agentic-patterns
description: Use when AI agent design principles. Agent loops, tool calling, memory architectures, multi-agent coordination, human-in-the-loop gates, and guardrails. Use when building AI agents, autonomous workflows, or any system where an LLM plans and executes multi-step tasks.
version: 5.0.0
last-updated: 2026-09-13
skills:
- agent-organizer
- fabel-protocol
- thinking-protocol
tools: Read, Grep, Glob, Bash, Edit, Write
scripts-binding:
- .agent/scripts/swarm_dispatcher.js
- .agent/scripts/verify_all.js
- .agent/scripts/lint_runner.js
---
# Agentic Patterns
## Mandatory Pre-Flight Context Inspection
Before generating, refactoring, or reviewing code in the `agentic-patterns` domain, inspect these 5 critical parameters:
1. **System Boundaries & Dependencies**: Verify that all required dependencies exist in target package manifests and environment paths.
2. **Runtime Context & Platform Invariants**: Confirm target platform constraints (Node.js, Browser, Mobile OS, Edge runtime) before applying APIs.
3. **Execution Guardrails**: Identify potential side-effects, state mutations, and unhandled asynchronous exceptions.
4. **Validation & Type Contracts**: Validate input data schemas and strict type constraints across all module interfaces.
5. **Observability & Proof of Execution**: Ensure execution produces tangible verification signals (terminal output, tests, metrics).
## Activation Boundaries
- **Activate when:** Use when AI agent design principles. Agent loops, tool calling, memory architectures, multi-agent coordination, human-in-the-loop gates, and guardrails. Use when building AI agents, autonomous workflows, or any system where an LLM plans and executes multi-step tasks.
- **DO NOT activate when:** The task falls outside the `agentic-patterns` domain or is managed by a different dedicated specialist.
## 🔁 Multi-Pass Execution Protocol
Execute all non-trivial tasks through this 7-pass cognitive loop:
| Pass | Phase | Core Action |
| :--------- | :--------------- | :------------------------------------------------------------------------------------------- |
| **Pass 1** | **Understand** | Deconstruct the user's explicit objective, implicit requirements, and platform constraints. |
| **Pass 2** | **Plan** | Decompose the task into smallest logical steps; map dependencies and required tool calls. |
| **Pass 3** | **Execute** | Implement the solution with production-grade craft, zero placeholders, and strict typing. |
| **Pass 4** | **Verify** | Run linters, unit tests, or compiler checks to validate structural correctness. |
| **Pass 5** | **Attack** | Perform an adversarial review searching for edge-case failures, race conditions, and traps. |
| **Pass 6** | **Improve** | Eliminate discovered friction, optimize performance, and harden error boundaries. |
| **Pass 7** | **Quality Gate** | Enforce Verification-Before-Completion (VBC) with concrete terminal proof before finalizing. |
---
## 🛠️ Technical Architecture & Reference Recipes
---
---
## The Agent Loop
Every AI agent follows this fundamental pattern:
```
PERCEIVE → PLAN → ACT → OBSERVE → (repeat or terminate)
1. PERCEIVE — What is the current state? What does the agent know?
2. PLAN — What action will move toward the goal?
3. ACT — Execute the tool, call the API, write the file
4. OBSERVE — What changed? Did the action succeed?
5. EVALUATE — Goal reached? Continue loop or return?
```
### When to Terminate
```ts
// The three termination conditions — always define all three
type AgentResult = {
reason: 'goal_reached' | 'max_steps_exceeded' | 'human_escalation';
steps: number;
result: string;
};
const MAX_STEPS = 10; // Hard cap — never let agents loop indefinitely
```
---
## Tool Calling Design
Tools are the agent's interface to the real world. Design them defensively:
```ts
// Tool definition — what the LLM sees and how to call it
const tools = [
{
type: 'function',
function: {
name: 'search_database',
description:
'Search the product database. Use this before creating a new record to avoid duplicates.',
parameters: {
type: 'object',
properties: {
query: {
type: 'string',
description: 'Search terms — be specific',
},
limit: {
type: 'number',
description: 'Max results to return. Default: 5, max: 20',
},
},
required: ['query'],
},
},
},
];
// Tool executor — validate before running
async function executeTool(name: string, args: unknown): Promise<string> {
// Validate args before executing — never trust LLM output directly
const parsed = ToolArgsSchema.safeParse(args);
if (!parsed.success) {
return `Error: Invalid arguments — ${parsed.error.message}`;
}
// Scope check — is this tool allowed for this agent's role?
if (!agentPermissions.includes(name)) {
return `Error: Tool '${name}' is not permitted for this agent`;
}
try {
return await tools[name](parsed.data);
} catch (err) {
return `Error: Tool execution failed — ${(err as Error).message}`;
}
}
```
---
## Memory Architecture
Agents need different types of memory for different purposes:
```
IN-CONTEXT MEMORY (cheapest, shortest-lived):
→ Current conversation + recent tool outputs
→ Limited by context window (~100k tokens)
→ Good for: current task context
EXTERNAL SEMANTIC MEMORY (vector search):
→ Long-term knowledge, past conversations
→ Unlimited, but retrieval is approximate
→ Good for: "What did we discuss about this topic before?"
EPISODIC MEMORY (structured log):
→ Exact record of past actions and outcomes
→ Good for: learning from past mistakes, auditability
PROCEDURAL MEMORY (system prompt + tools):
→ How the agent knows to behave and what it can do
→ Good for: skills, personas, behavior rules
```
```ts
// External memory: retrieve relevant past context before each turn
async function buildContext(userId: string, currentQuery: string) {
const queryEmbedding = await embed(currentQuery);
// Retrieve semantically relevant past interactions
const pastMemories = await vectorDB.search({
query: queryEmbedding,
filter: { userId },
limit: 5,
});
return [
{ role: 'system', content: systemPrompt },
// Inject relevant past context — NOT entire history
{
role: 'system',
content: `Relevant past context:\n${pastMemories.map(m => m.content).join('\n')}`,
},
{ role: 'user', content: currentQuery },
];
}
```
---
## Multi-Agent Coordination Patterns
When a task requires multiple specialists:
### Supervisor Pattern
```
Supervisor agent ─→ breaks task into subtasks
│
├─→ Research agent (reads, gathers information)
├─→ Writer agent (drafts based on research)
└─→ Reviewer agent (critiques the draft)
│
└─→ Supervisor collects results, makes final decision
```
### Peer Review Pattern (Anti-Hallucination for Agents)
```ts
// Two independent agents answer the same question — supervisor resolves disagreement
const [answerA, answerB] = await Promise.all([
agentA.complete(question),
agentB.complete(question),
]);
if (answerA.answer === answerB.answer) {
return answerA; // Agreement — high confidence
}
// Disagreement — escalate to human or third tiebreaker
return await supervisor.resolve(question, answerA, answerB);
```
---
## Human-in-the-Loop Gates
The most important agentic pattern. Agents should request human approval before:
- Deleting data
- Sending external communications (emails, webhooks)
- Spending real money (API calls with cost, purchases)
- Making irreversible changes
- Acting on low-confidence decisions
```ts
async function agentLoop(task: string) {
for (let step = 0; step < MAX_STEPS; step++) {
const planned = await llm.plan(task, history);
// ✅ Human gate before irreversible actions
if (planned.action.isIrreversible) {
const approved = await requestHumanApproval({
action: planned.action,
reason: planned.reasoning,
confidence: planned.confidence,
});
if (!approved) return { reason: 'human_rejected', step };
}
// ✅ Confidence gate — don't act when uncertain
if (planned.confidence < 0.7) {
return {
reason: 'human_escalation',
message: `Low confidence (${planned.confidence}) on: ${planned.action.description}`,
};
}
const result = await executeTool(planned.action.tool, planned.action.args);
history.push({ action: planned.action, result });
if (planned.goalReached) break;
}
}
```
---
## Guardrails
Every production agent needs:
```ts
const guardrails = {
// Input guardrails — reject bad prompts before they reach the agent
input: [
{ check: 'no_prompt_injection', action: 'reject' },
{ check: 'within_scope', action: 'reject' }, // Off-topic requests
{ check: 'pii_detection', action: 'redact' }, // Redact before processing
],
// Output guardrails — validate before returning
output: [
{ check: 'no_hallucinated_citations', action: 'flag' },
{ check: 'schema_valid', action: 'retry_once' },
{ check: 'no_pii_leaked', action: 'reject' },
],
// Resource guardrails — prevent runaway cost/loops
resource: [
{ check: 'max_tokens_per_session', limit: 100_000 },
{ check: 'max_tool_calls_per_session', limit: 50 },
{ check: 'max_cost_per_session_usd', limit: 1.0 },
],
};
```
---
## Output Format
When this skill completes a task, structure your output as:
```
━━━ Agentic Patterns Output ━━━━━━━━━━━━━━━━━━━━━━━━
Task: [what was performed]
Result: [outcome summary — one line]
─────────────────────────────────────────────────
Checks: ✅ [N passed] · ⚠️ [N warnings] · ❌ [N blocked]
VBC status: PENDING → VERIFIED
Evidence: [link to terminal output, test result, or file diff]
```
---
## 🚨 Edge-Case & Failure Mode Matrix
| Scenario | Risk | Mitigation Strategy |
| :------------------------------------- | :--------------------------------------------------- | :------------------------------------------------------------------------------ |
| **Empty or Null Inputs** | Unhandled exception or unexpected rendering collapse | Enforce fallback guards, optional chaining, and explicit empty state handlers |
| **Network Timeout / Latency** | Hanging operations or duplicate side-effects | Implement bounded abort controllers, exponential backoff, and idempotency keys |
| **Concurrency / Race Conditions** | Stale state overwrite or inconsistent data mutations | Use atomic transactions, mutex locking, or cancel-on-resubmit controls |
| **Invalid Schema / Malformed Payload** | Downstream runtime errors or security injection | Validate boundary payloads with Zod/Pydantic schemas prior to execution |
| **Resource / Memory Saturation** | OOM errors, frame drops, or memory leaks | Clean up listeners, cancel active timers, and enforce pagination/virtualization |
---
## 🤖 LLM-Specific Traps Table
| Anti-Pattern | What AI Commonly Does Wrong | What Is Actually Correct |
| :--------------------------------- | :------------------------------------------------------------------------ | :----------------------------------------------------------------------- |
| **Hallucinated Tool Capabilities** | Assuming an external library or CLI command exists without verification | Run a verification check or verify package.json before referencing tools |
| **Premature Completion Claim** | Declaring a task finished because code was generated without verification | Execute tests, linters, or terminal commands to provide concrete proof |
| **Context Bloat Dumping** | Pasting entire multi-thousand-line files into prompt context | Extract targeted excerpts, symbols, and signatures to preserve tokens |
---
## 🏛️ Tribunal Verification & Guardrails
**Active Reviewers:** `orchestrator` · `agent-organizer` · `logic-reviewer`
**Slash Command:** `/review` or `/tribunal-full`
### ✅ Pre-Flight Self-Audit Checklist
```
✅ Did I deconstruct the root objective before proposing architecture?
✅ Did I identify dependencies, bottlenecks, and parallelizable sub-tasks?
✅ Did I avoid over-engineering and select the simplest effective pattern?
✅ Did I verify assumptions with concrete file reads instead of speculation?
✅ Did I establish measurable verification criteria before completion?
```
### 🛑 Verification-Before-Completion (VBC) Protocol
**CRITICAL:** You must follow a strict "evidence-based closeout" state machine.
- ❌ **Forbidden:** Declaring a task complete because the output "looks correct."
- ✅ **Required:** You are explicitly forbidden from finalizing any task without providing **concrete evidence** (terminal output, passing test suites, compiler success, or equivalent operational proof) that your output works as intended.