1 min read
▶ Video
What we learned shipping AI agents to production
Agents are fun in a demo and humbling in production. Here are the guardrails that made ours reliable enough to trust.
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8 min read
Building reliable tools for LLMs
Function calling, agents, and MCP all rest on the same thing: tools an LLM can invoke. But a tool built for a non-deterministic, fallible caller needs different design than one built for code. A deep-dive on what makes a tool reliable for an LLM — naming, scope, validation, error messages it can recover from, and the safety that can't live in the model.
7 min read
Evaluating and monitoring agents in production
Pre-launch evals tell you an agent works on your test set. Production tells you whether it works on reality — and only continuous monitoring closes that gap. A deep-dive on observing agents in the wild: what to log, what to measure, how to catch silent degradation, and how production failures feed back into your evals.
6 min read
Multi-agent systems: orchestration patterns
Once single agents work, the temptation is to wire several together — a researcher, a writer, a critic — and let them collaborate. Sometimes that's powerful; often it's complexity you didn't need. A deep-dive on the orchestration patterns for multi-agent systems, when they earn their keep, and the failure modes that multiply when agents multiply.
7 min read
Spec-driven development with AI agents
When an AI agent does the implementation, the specification becomes the highest-leverage artifact in the project — a clear spec produces good code, a vague one produces a confident mess. A deep-dive on why spec-driven development has resurged in the agent era, what a good spec for an agent looks like, and how the workflow actually changes.
7 min read
Guardrails and evals for production agents
Putting an agent in production is harder than a single LLM call, because an agent is a loop with a multiplied failure surface — and you have to both contain it at runtime and measure it over time. A deep-dive on the two disciplines that make production agents trustworthy: guardrails that bound what an agent can do, and evals that test a whole trajectory, not a single output.