agentic-ai-dev

name	agentic-ai-dev
description	This skill provides patterns and templates for building production AI agents with Python 3.14, LangChain v1.2.8, LangGraph v1.0.7, and FastAPI 0.135.2. Use when creating AI agents, RAG systems, graph workflows, tools, memory systems, or agent tests.
allowed-tools	Bash, Read, Write, Edit
metadata	{"triggers":"AI agent, LangChain, LangGraph, RAG system, graph workflow, FastAPI, Python 3.14, LangGraph agent, memory system, agent test","related-skills":"agentic-ai-coding-standard, python-dev, mcp-builder","domain":"backend","role":"specialist","scope":"implementation","output-format":"code"}
last-reviewed	2026-03-14

Iron Law

NO AGENT GRAPH WITHOUT AN ITERATION LIMIT AND A HUMAN-IN-THE-LOOP CHECKPOINT — unbounded loops and silent runaway agents are production incidents

Agentic AI Development Skill — Python 3.14 + LangChain + LangGraph + FastAPI

Quick Scaffold

uv init my-agent-service && cd my-agent-service
uv add "langchain-core>=1.2.8" "langchain-anthropic>=1.3.0" "langchain-openai>=1.1.0" "langgraph>=1.0.7" \
  "fastapi>=0.135.2" "uvicorn[standard]" pydantic pydantic-settings \
  langsmith prometheus-client structlog httpx asyncpg \
  "langgraph-checkpoint-postgres>=3.0.0"
uv add --dev pytest pytest-asyncio httpx ruff mypy

Process

1. Scaffold — uv init + install dependencies
2. Configure — core/config.py with pydantic-settings, .env, structured logging
3. Define State — TypedDict with Annotated[list, add_messages] for each agent
4. Build Graph — StateGraph with typed nodes, conditional edges, checkpointing
5. Define Tools — @tool with docstrings, Pydantic input schemas, error handling
6. Add Memory — Checkpointing (PostgresSaver), semantic memory (vector store)
7. Add Guardrails — Input validation, prompt injection detection, output validation
8. Expose API — FastAPI routes for invoke/stream with thread_id propagation
9. Write Tests — Basic invoke, tool usage, iteration limit, error recovery, RAG quality
10. Deploy — Docker multi-stage, gunicorn + uvicorn, health checks, Prometheus

Key Patterns

Pattern	Implementation	Reference
Agent Graphs	StateGraph + typed nodes + conditional edges	agentic-templates-basic.md
Tools	@tool + docstring + Pydantic input + try/except	agentic-templates-tools.md
LLM Binding	Factory function per provider, .bind_tools()	agentic-llm-routing.md
Routing	Command(goto=...) pattern (LangGraph)	agentic-templates-advanced.md
Checkpointing	PostgresSaver (prod) / MemorySaver (test)	agentic-memory-systems.md
Streaming	astream() + stream_mode + FastAPI SSE	agentic-streaming-hitl.md
Human-in-the-Loop	interrupt_before + approval node	agentic-streaming-hitl.md
RAG	Embeddings → Vector Store → Retriever → Reranker	agentic-templates-rag.md
Guardrails	12-layer pipeline: input → process → output	agentic-guardrails-security.md
Structured Output	.with_structured_output(PydanticModel)	agentic-prompt-engineering.md
Error Recovery	Retry node + fallback model + graceful degradation	agentic-templates-resilience.md
Config	pydantic-settings + fail-fast validators	agentic-config-project.md
Caching	4-tier Q1→Q2→Q3→L3 with backfill; @cached_tool decorator	agentic-caching-patterns.md

Conventions & Rules

For package layout, LangGraph rules, and FastAPI integration rules, read reference/agentic-conventions.md

Documentation Sources

Before generating code, consult these sources for current syntax and APIs:

Source	URL / Tool	Purpose
LangGraph	https://langchain-ai.github.io/langgraph/llms-full.txt	StateGraph, nodes, edges, checkpointing APIs
Pydantic v2	https://docs.pydantic.dev/latest/llms-full.txt	Model validation, settings, Field constraints
FastAPI / LangChain	Context7 MCP	Latest LangChain tools, FastAPI patterns

Reference Files

File	Content	When to Use
agentic-agent-variant-ladder.md	Capability tier pattern, deterministic shadow agents, NDJSON replay	Multi-agent architecture
agentic-config-project.md	pyproject.toml, .env, config, Docker, ruff/mypy	Project setup
agentic-templates-core.md	FastAPI app, main.py, routes, middleware, base state	Creating API layer
agentic-templates-basic.md	ReAct Agent, Multi-Agent Collaborative patterns	Building basic agents
agentic-templates-advanced.md	Hierarchical Supervisor, Command, Sub-Graph patterns	Building complex agents
agentic-templates-resilience.md	Error Recovery Agent, key design decisions	Agent error handling
agentic-templates-rag.md	6 RAG architectures + document ingestion pipeline	Building RAG systems
agentic-templates-tools.md	@tool patterns, MCP integration, retry/timeout	Defining agent tools
agentic-guardrails-security.md	12-layer security framework	Adding safety layers
agentic-memory-systems.md	7-layer memory hierarchy, practical implementations	Adding memory to agents
agentic-streaming-hitl.md	Streaming + Human-in-the-Loop patterns	Real-time responses, approval flows
agentic-llm-routing.md	Multi-provider routing, cost calculation, fallback chains	Multi-model setups
agentic-observability.md	LangSmith, Prometheus, structured logging	Monitoring and debugging
agentic-testing.md	Agent testing patterns, mocks, fixtures	Writing agent tests
agentic-deployment.md	Docker, docker-compose, production config	Deploying agents

Common Commands

uvicorn src.main:app --reload                          # Run dev server (hot reload)
pytest -q                                              # Run tests (quiet output)
pytest -q --cov=src --cov-report=term-missing          # Tests with coverage
ruff check --fix .                                     # Lint and auto-fix
ruff format .                                          # Format code
mypy src/                                              # Type check

Error Handling

For error handling patterns and code examples, read reference/agentic-error-handling.md

LLM provider errors: Use retry with exponential backoff + fallback model chain. Never let provider errors crash the graph.

Tool execution errors: Wrap all @tool functions in try/except. Return structured error messages the LLM can reason about.

Graph infinite loops: Always include iteration_count in state and check it in the routing function.

Post-Code Review

After writing agentic AI code, dispatch these reviewer agents:

• agentic-ai-reviewer — graph correctness, guardrails, iteration limits, cost efficiency
• security-reviewer — tool input validation, prompt injection defense