Agent loops, tool use, function calling, LangChain, and multi-agent systems. 12 articles, written by the engineers building AI App Lab.
AI agent approval gate: human-in-the-loop HITL for sensitive tools. OpenAI Agents SDK interruptions, LangGraph interrupt primitive, Deep Agents interrupt_on, RunState serialize/resume, approval patterns, and production checklist for 2026.
Multi-agent systems for LLMs: coordination patterns, network topologies (hierarchical, flat, supervisor), role specialization, agent handoffs, MetaGPT, CAMEL, CrewAI, AutoGen, LangGraph, OpenAI Agents SDK, and production checklist for 2026.
LangChain AI agent: create_agent (v1.0 standard), create_react_agent deprecated, AgentExecutor legacy, middleware system, LangGraph runtime, tools, checkpointer persistence, human-in-the-loop, structured output, and production checklist for 2026.
What is an AI agent: autonomous LLM systems with perception, reasoning, planning, action, tool use, and memory. ReAct pattern, agent taxonomy, single vs multi-agent, agentic AI vs traditional LLM, frameworks, and production checklist for 2026.
Schedule AI agent cron jobs: APScheduler, Celery Beat, system crontab, periodic agent execution, state management across runs, failure handling, episodic memory, Docker deployment, and production checklist for 2026.
LLM function calling: OpenAI tools API, Anthropic tool use, Gemini function calling. Parallel tool calls, tool_choice modes, JSON schema parameters, structured outputs, multi-step tool chains, MCP standardization, and production checklist for 2026.
AI agent tool loop: ReAct reason-act-observe-reflect pattern, loop engineering, termination conditions, max steps, no-progress detection, circuit breakers, multi-step tool chaining, and production checklist for 2026.
Build an AI agent from scratch in Python: no framework, just OpenAI function calling, tool use loop, conversation memory, system prompt, planning, multi-tool orchestration, error handling, and production checklist for 2026.
Debug AI agents: LangSmith tracing, OpenTelemetry instrumentation, step-level observability, common failures (infinite loops, hallucinations, tool errors), log analysis, AgentPrism visualization, and production checklist for 2026.
AI agent memory: three-tier architecture (short-term context, working memory, long-term storage), vector databases for semantic retrieval, summarization, sliding window, AgeMem unified framework, episodic/semantic/procedural memory, and production checklist for 2026.
AI agent web search: Tavily LLM-native, Brave Search API, Exa neural search, Serper Google SERP, DuckDuckGo. Search tool integration, result parsing, citation validation, RAG, and production checklist for 2026.
AI agent without framework: no LangChain, no CrewAI, no AutoGen. Vanilla Python with OpenAI SDK, raw API calls, custom tool loop, minimal dependencies, full control, explainability, and production checklist for 2026.