Systems that run while you sleep.
Deterministic multi-agent systems with explicit state machines, sandboxed tool runners, and reflection loops that self-correct.
AI Agents Why Pass Rate Lies: Revision Rate, Trajectories, and Coverage
Pass rate flatters bad agents. Gate deploys on revision rate, trajectory scores, eval coverage, and cost per successful task—not a single green percentage.
AI Agents Why Agents Loop on Failed Tools: No-Progress Detection Beats Longer Prompts
Agents loop on failed tools because the harness never detects no-progress. Fingerprint calls, honor retryable:false, cap turns, and terminate with a reason code.
AI Agents Agentic Systems: An Operating Manual for Multi-Agent Work That Ships
An agentic system is not a chat window with tools. It is evaluators, sandboxes, state machines, memory contracts, and kill switches — built so the work survives contact with real data.
AI Agents Why Agent Demos Die in Production: Control Gaps, Not Model IQ
Demo success proves a happy path under staged tools. Production fails when the control loop — schemas, auth, evaluators, and kill switches — was never part of the demo.
AI Agents Build the Evaluator Before the Agent
If judgement and work share a context, you are grading your own homework. Build the evaluator first — criteria, evidence, ceilings — then let the agent earn autonomy.
AI Agents The Evaluator Is the Product
Agent accuracy did not come from a better prompt or a bigger model. It came from separating the thing that does the work from the thing that judges it.
AI Agents Sandboxed Tool Use: Letting Agents Act Without Letting Them Loose
Tool use without a sandbox is an API key with opinions. Allowlists, scoped credentials, blast-radius caps, and human gates are how agents earn the right to touch production.
AI Agents Single Agent First: Split Only When Trust, Audience, or Timing Conflicts
Start with one agent and many tools. Split only when trust, audience, or timing conflict—and prove that split with pass rate, cost, and escalate rate.
AI Agents State Machines for Agent Loops: Determinism Where It Matters
Agent loops need freedom inside a cage. Explicit states, legal transitions, revision ceilings, and escalate paths are how non-deterministic models ship deterministic ops.
AI Agents MCP vs Native Function Calling: Portability Tax vs Shortest Loop
Native function calling wins for one app's short tool loop; MCP earns the tax when tools must be shared and governed across hosts—not a LangChain swap.
AI Agents RAG That Does Not Lie: Retrieval Contracts for Business Knowledge
Retrieval is search, not truth. Production RAG needs corpora rules, citation requirements, refuse-on-empty behavior, and contradiction handling — or your agent will invent policy with confidence.
AI Agents LangGraph vs CrewAI vs a Custom Loop: Choose Control, Not Fashion
Pick LangGraph, CrewAI, or a custom loop by how much control you need—then compare them on the same golden set and cost band, not on Hacker News fashion.