Reference
Guides
Practical, citable material for designing, evaluating, and operating agentic systems.
Foundations
Practical guardrails for agentic AI: permissions, sandboxes, human approval, budgets, content filters, and audit trails.
A practical framework for evaluating agentic systems: task success, trajectories, tool correctness, cost, latency, and regression suites.
When to use agentic systems, chatbots, or deterministic workflows—decision criteria, trade-offs, and hybrid patterns.
A practical architecture model for agentic systems: goals, planners, tools, memory, orchestration, evals, and control planes.
Agentic AI systems pursue goals by planning, using tools, observing results, and iterating—unlike single-turn chatbots or fixed workflows.
Field guides
Agent Memory Explained
Working memory, thread memory, long-term memory, and RAG—how memory works in agentic systems and how to govern it.
Building Coding Agents: Architecture and Guardrails
How coding agents work: repo tools, planning, tests as rewards, sandboxes, and evaluation for software engineering agents.
How to Choose an Agent Framework
A practical framework selection guide for agentic AI: control model, language, multi-agent needs, ecosystem, and production constraints.
Observability for AI Agents
What to log and monitor in agentic systems: trajectories, tool I/O, costs, outcomes, and replay for debugging.
Common AI Agent Failure Modes
The most frequent ways agents fail in production: loops, tool misuse, hallucinated success, injection, and scope escape—and how to mitigate them.
Agent Cost Control: Budgets, Caching, and Model Routing
How to control token and tool spend in agentic systems without destroying task success rates.
MCP Explained: Model Context Protocol for Agents
What MCP (Model Context Protocol) is, why it matters for agent tool use, and how to think about servers, clients, and security.
LangGraph vs CrewAI vs AutoGen
Vendor-neutral comparison of LangGraph, CrewAI, and AutoGen for building multi-step and multi-agent systems.
How to Build a Research Agent
Step-by-step design for a web research agent: goals, tools, planning, citations, evaluation, and safety limits.
AI Agent Production Readiness Checklist
A practical checklist for shipping agentic systems: tools, auth, budgets, evals, observability, HITL, and incident response.