AI Agents Development — Production Skill Hub

AI Agents Development — Production Skill Hub

Modern Best Practices (January 2026): deterministic control flow, bounded tools, auditable state, MCP-based tool integration, handoff-first orchestration, multi-layer guardrails, OpenTelemetry tracing, and human-in-the-loop controls (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/).

This skill provides production-ready operational patterns for designing, building, evaluating, and deploying AI agents. It centralizes procedures, checklists, decision rules, and templates used across RAG agents, tool-using agents, OS agents, and multi-agent systems.

No theory. No narrative. Only operational steps and templates.

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When to Use This Skill

Codex should activate this skill whenever the user asks for:

• Designing an agent (LLM-based, tool-based, OS-based, or multi-agent).
• Scoping capability maturity and rollout risk for new agent behaviors.
• Creating action loops, plans, workflows, or delegation logic.
• Writing tool definitions, MCP tools, schemas, or validation logic.
• Generating RAG pipelines, retrieval modules, or context injection.
• Building memory systems (session, long-term, episodic, task).
• Creating evaluation harnesses, observability plans, or safety gates.
• Preparing CI/​CD, rollout, deployment, or production operational specs.
• Producing any template in /​references/ or /​assets/.
• Implementing MCP servers or integrating Model Context Protocol.
• Setting up agent handoffs and orchestration patterns.
• Configuring multi-layer guardrails and safety controls.
• Evaluating whether to build an agent (build vs not decision).
• Calculating agent ROI, token costs, or cost/​benefit analysis.
• Assessing hallucination risk and mitigation strategies.
• Deciding when to kill an agent project (kill triggers).
• For prompt scaffolds, retrieval tuning, or security depth, see Scope Boundaries below.

Scope Boundaries (Use These Skills for Depth)

• Prompt scaffolds & structured outputs → ai-prompt-engineering
• RAG retrieval & chunking → ai-rag
• Search tuning (BM25/​HNSW/​hybrid) → ai-rag
• Security/​guardrails → ai-mlops
• Inference optimization → ai-llm-inference

Default Workflow (Production)

• Pick an architecture with the Decision Tree (below); default to workflow/​FSM/​DAG for production.
• Draft an agent spec with assets/​core/​agent-template-standard.md (or assets/​core/​agent-template-quick.md).
• Specify tools and handoffs with JSON Schema using assets/​tools/​tool-definition.md and references/​api-contracts-for-agents.md.
• Add retrieval only when needed; start with assets/​rag/​rag-basic.md and scale via assets/​rag/​rag-advanced.md + references/​rag-patterns.md.
• Add eval + telemetry early via references/​evaluation-and-observability.md.
• Run the go/​no-go gate with assets/​checklists/​agent-safety-checklist.md.
• Plan deploy/​rollback and safety controls via references/​deployment-ci-cd-and-safety.md.

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Quick Reference

Framework Selection (2026)

See references/​modern-best-practices.md for detailed framework comparison and selection guide.

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Decision Tree: Choosing Agent Architecture

What does the agent need to do?
    ├─ Answer questions from knowledge base?
    │   ├─ Simple lookup? → RAG Agent (LangChain/​LlamaIndex + vector DB)
    │   └─ Complex multi-step? → Agentic RAG (iterative retrieval + reasoning)
    │
    ├─ Perform external actions (APIs, tools, functions)?
    │   ├─ 1-3 tools, linear flow? → Tool-Using Agent (LangGraph + MCP)
    │   └─ Complex workflows, branching? → Planning Agent (ReAct/​Plan-Execute)
    │
    ├─ Write/​modify code autonomously?
    │   ├─ Single file edits? → Tool-Using Agent with code tools
    │   └─ Multi-file, issue resolution? → Code/​SWE Agent (HyperAgent pattern)
    │
    ├─ Delegate tasks to specialists?
    │   ├─ Fixed workflow? → Multi-Agent Sequential (A → B → C)
    │   ├─ Manager-Worker? → Multi-Agent Hierarchical (Manager + Workers)
    │   └─ Dynamic routing? → Multi-Agent Group Chat (collaborative)
    │
    ├─ Control desktop/​browser?
    │   └─ OS Agent (Anthropic Computer Use + MCP for system access)
    │
    └─ Hybrid (combination of above)?
        └─ Planning Agent that coordinates:
            - Tool-using for actions (MCP)
            - RAG for knowledge (MCP)
            - Multi-agent for delegation (A2A)
            - Code agents for implementation

Protocol Selection:

• Use MCP for: Tool access, data retrieval, single-agent integration
• Use A2A for: Agent-to-agent handoffs, multi-agent coordination, task delegation

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Core Concepts (Vendor-Agnostic)

Control Flow Options

• Reactive: direct tool routing per user request (fast, brittle if unbounded).
• Workflow (FSM/​DAG): explicit states and transitions (default for deterministic production).
• Planner/​Executor: plan with strict budgets, then execute step-by-step (use when branching is unavoidable).
• Orchestrated multi-agent: separate roles with validated handoffs (use when specialization is required).

Memory Types (Tradeoffs)

• Short-term (session): cheap, ephemeral; best for conversational continuity.
• Episodic (task): scoped to a case/​ticket; supports audit and replay.
• Long-term (profile/​knowledge): high risk; requires consent, retention limits, and provenance.

Failure Handling (Production Defaults)

• Classify errors: retriable vs fatal vs needs-human.
• Bound retries: max attempts, backoff, jitter; avoid retry storms.
• Fallbacks: degraded mode, smaller model, cached answers, or safe refusal.

Do / Avoid

Do

• Do keep state explicit and serializable (replayable runs).
• Do enforce tool allowlists, scopes, and idempotency for side effects.
• Do log traces/​metrics for model calls and tool calls (OpenTelemetry GenAI semantic conventions: https://opentelemetry.io/docs/specs/semconv/gen-ai/).

Avoid

• Avoid runaway autonomy (unbounded loops or step counts).
• Avoid hidden state (implicit memory that cannot be audited).
• Avoid untrusted tool outputs without validation/​sanitization.

Navigation: Economics & Decision Framework

Should You Build an Agent?

• Build vs Not Decision Framework - references/​build-vs-not-decision.md
  • 10-second test (volume, cost, error tolerance)
  • Red flags and immediate disqualifiers
  • Alternatives to agents (usually better)
  • Full decision tree with stage gates
  • Kill triggers during development and post-launch
  • Pre-build validation checklist

Agent ROI & Token Economics

• Agent Economics - references/​agent-economics.md
  • Token pricing by model (January 2026)
  • Cost per task by agent type
  • ROI calculation formula and tiers
  • Hallucination cost framework and mitigation ROI
  • Investment decision matrix
  • Monthly tracking dashboard

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Navigation: Core Concepts & Patterns

Governance & Maturity

• Agent Maturity & Governance - references/​agent-maturity-governance.md
  • Capability maturity levels (L0-L4)
  • Identity & policy enforcement
  • Fleet control and registry management
  • Deprecation rules and kill switches

Modern Best Practices

• Modern Best Practices - references/​modern-best-practices.md
  • Model Context Protocol (MCP)
  • Agent-to-Agent Protocol (A2A)
  • Agentic RAG (Dynamic Retrieval)
  • Multi-layer guardrails
  • LangGraph over LangChain
  • OpenTelemetry for agents

Context Management

• Context Engineering - references/​context-engineering.md
  • Progressive disclosure
  • Session management
  • Memory provenance
  • Retrieval timing
  • Multimodal context

Core Operational Patterns

• Operational Patterns - references/​operational-patterns.md
  • Agent loop pattern (PLAN → ACT → OBSERVE → UPDATE)
  • OS agent action loop
  • RAG pipeline pattern
  • Tool specification
  • Memory system pattern
  • Multi-agent workflow
  • Safety & guardrails
  • Observability
  • Evaluation patterns
  • Deployment & CI/​CD

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Navigation: Protocol Implementation

• MCP Practical Guide - references/​mcp-practical-guide.md Building MCP servers, tool integration, and standardized data access

• MCP Server Builder - references/​mcp-server-builder.md End-to-end checklist for workflow-focused MCP servers (design → build → test)

• A2A Handoff Patterns - references/​a2a-handoff-patterns.md Agent-to-agent communication, task delegation, and coordination protocols

• Protocol Decision Tree - references/​protocol-decision-tree.md When to use MCP vs A2A, decision framework, and selection criteria

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Navigation: Agent Capabilities

• Agent Operations - references/​agent-operations-best-practices.md Action loops, planning, observation, and execution patterns

• RAG Patterns - references/​rag-patterns.md Contextual retrieval, agentic RAG, and hybrid search strategies

• Memory Systems - references/​memory-systems.md Session, long-term, episodic, and task memory architectures

• Tool Design & Validation - references/​tool-design-specs.md Tool schemas, validation, error handling, and MCP integration

Skill Packaging & Sharing

• Skill Lifecycle - references/​skill-lifecycle.md Scaffold, validate, package, and share skills with teams (Slack-ready)

• API Contracts for Agents - references/​api-contracts-for-agents.md Request/​response envelopes, safety gates, streaming/​async patterns, error taxonomy

• Multi-Agent Patterns - references/​multi-agent-patterns.md Manager-worker, sequential, handoff, and group chat orchestration

• OS Agent Capabilities - references/​os-agent-capabilities.md Desktop automation, UI grounding, and computer use patterns

• Code/​SWE Agents - references/​code-swe-agents.md SE 3.0 paradigm, autonomous coding patterns, SWE-Bench, HyperAgent architecture

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Navigation: Production Operations

• Evaluation & Observability - references/​evaluation-and-observability.md OpenTelemetry GenAI, metrics, LLM-as-judge, and monitoring

• Deployment, CI/​CD & Safety - references/​deployment-ci-cd-and-safety.md Multi-layer guardrails, HITL controls, NIST AI RMF, production checklists

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Navigation: Templates (Copy-Paste Ready)

Checklists

• Agent Design & Safety Checklist - assets/​checklists/​agent-safety-checklist.md Go/​No-Go safety gate: permissions, HITL triggers, eval gates, observability, rollback

Core Agent Templates

• Standard Agent Template - assets/​core/​agent-template-standard.md Full production spec: memory, tools, RAG, evaluation, observability, safety

• Specialized Agent Template - assets/​core/​agent-template-specialized.md Domain-specific agents with custom capabilities and constraints

• Quick Agent Template - assets/​core/​agent-template-quick.md Minimal viable agent for rapid prototyping

RAG Templates

• Basic RAG - assets/​rag/​rag-basic.md Simple retrieval-augmented generation pipeline

• Advanced RAG - assets/​rag/​rag-advanced.md Contextual retrieval, reranking, and agentic RAG patterns

• Hybrid Retrieval - assets/​rag/​hybrid-retrieval.md Semantic + keyword search with BM25 fusion

Tool Templates

• Tool Definition - assets/​tools/​tool-definition.md MCP-compatible tool schemas with validation and error handling

• Tool Validation Checklist - assets/​tools/​tool-validation-checklist.md Testing, security, and production readiness checks

Multi-Agent Templates

• Manager-Worker Template - assets/​multi-agent/​manager-worker-template.md Orchestration pattern with task delegation and result aggregation

• Evaluator-Router Template - assets/​multi-agent/​evaluator-router-template.md Dynamic routing with quality assessment and domain classification

Service Layer Templates

• FastAPI Agent Service - ../​dev-api-design/​assets/​fastapi/​fastapi-complete-api.md Auth, pagination, validation, error handling; extend with model lifespan loads, SSE, background tasks

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External Sources Metadata

• Curated References - data/​sources.json Authoritative sources spanning standards, protocols, and production agent frameworks

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Shared Utilities (Centralized patterns — extract, don't duplicate)

• ../​software-clean-code-standard/​utilities/​llm-utilities.md — Token counting, streaming, cost estimation
• ../​software-clean-code-standard/​utilities/​error-handling.md — Effect Result types, correlation IDs
• ../​software-clean-code-standard/​utilities/​resilience-utilities.md — p-retry v6, circuit breaker for API calls
• ../​software-clean-code-standard/​utilities/​logging-utilities.md — pino v9 + OpenTelemetry integration
• ../​software-clean-code-standard/​utilities/​observability-utilities.md — OpenTelemetry SDK, tracing, metrics
• ../​software-clean-code-standard/​utilities/​testing-utilities.md — Test factories, fixtures, mocks
• ../​software-clean-code-standard/​references/​clean-code-standard.md — Canonical clean code rules (CC-*) for citation

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Trend Awareness Protocol

IMPORTANT: When users ask recommendation questions about AI agents, you MUST use WebSearch to check current trends before answering. If WebSearch is unavailable, use data/​sources.json + any available web browsing tools, and explicitly state what you verified vs assumed.

Trigger Conditions

• "What's the best agent framework for [use case]?"
• "What should I use for [multi-agent/​tool use/​orchestration]?"
• "What's the latest in AI agents?"
• "Current best practices for [agent architecture/​MCP/​A2A]?"
• "Is [LangGraph/​CrewAI/​AutoGen] still relevant in 2026?"
• "[Agent framework A] vs [Agent framework B]?"
• "Best way to build [coding agent/​RAG agent/​OS agent]?"
• "What MCP servers are available?"

Required Searches

1. Search: "AI agent frameworks best practices 2026"
2. Search: "[LangGraph/​CrewAI/​AutoGen/​Semantic Kernel] comparison 2026"
3. Search: "AI agent trends January 2026"
4. Search: "MCP servers available 2026"

What to Report

After searching, provide:

• Current landscape: What agent frameworks are popular NOW
• Emerging trends: New patterns gaining traction (MCP, A2A, agentic coding)
• Deprecated/​declining: Frameworks or patterns losing relevance
• Recommendation: Based on fresh data, not just static knowledge

Example Topics (verify with fresh search)

• Agent frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, Pydantic AI)
• MCP ecosystem (available servers, new integrations)
• Agentic coding (Codex CLI, Claude Code, Cursor, Windsurf, Cline)
• Multi-agent patterns (hierarchical, collaborative, competitive)
• Tool use protocols (MCP, function calling)
• Agent evaluation (SWE-Bench, AgentBench, GAIA)
• OS/​computer use agents (computer-use APIs, browser automation)

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Related Skills

This skill integrates with complementary skills:

Core Dependencies

• ../​ai-llm/ - LLM patterns, prompt engineering, and model selection for agents
• ../​ai-rag/ - Deep RAG implementation: chunking, embedding, reranking
• ../​ai-prompt-engineering/ - System prompt design, few-shot patterns, reasoning strategies

Production & Operations

• ../​qa-observability/ - OpenTelemetry, metrics, distributed tracing
• ../​software-security-appsec/ - OWASP Top 10, input validation, secure tool design
• ../​ops-devops-platform/ - CI/​CD pipelines, deployment strategies, infrastructure

Supporting Patterns

• ../​dev-api-design/ - REST/​GraphQL design for agent APIs and tool interfaces
• ../​ai-mlops/ - Model deployment, monitoring, drift detection
• ../​qa-debugging/ - Agent debugging, error analysis, root cause investigation

Usage pattern: Start here for agent architecture, then reference specialized skills for deep implementation details.

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Usage Notes

• Modern Standards: Default to MCP for tools, agentic RAG for retrieval, handoff-first for multi-agent
• Lightweight SKILL.md: Use this file for quick reference and navigation
• Drill-down resources: Reference detailed resources for implementation guidance
• Copy-paste templates: Use templates when the user asks for structured artifacts
• External sources: Reference data/​sources.json for authoritative documentation links
• No theory: Never include theoretical explanations; only operational steps

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Key Modern Migrations

Traditional → Modern:

• Custom APIs → Model Context Protocol (MCP)
• Static RAG → Agentic RAG with contextual retrieval
• Ad-hoc handoffs → Versioned handoff APIs with JSON Schema
• Single guardrail → Multi-layer defense (5+ layers)
• LangChain agents → LangGraph stateful workflows
• Custom observability → OpenTelemetry GenAI standards
• Model-centric → Context engineering-centric

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AI-Native SDLC Template

• Use assets/​agent-template-ainative-sdlc.md for the Delegate → Review → Own runbook (guardrails + outputs checklist).