AGENTSDo you need an AI agent, or just a workflow?
Agents decide their own steps; workflows follow yours. A decision guide for choosing the simplest architecture that solves the problem.
AGENTSAI agent failure modes: a field guide and postmortem template
Wrong arguments, irreversible actions, runaway loops and confident false success. How agents fail, how to investigate it, and which guardrail stops each failure.
AGENTSDesigning tools that AI agents can actually use
Most agent failures start with an ambiguous tool. Naming, schemas, error messages and permissions that make function calling reliable.
AGENTSTool-use evals are broken. Here's how to fix them.
Exact-match grading, single runs and perfect mock APIs make tool-use scores look better than reality. A better harness design, step by step.
AGENTSStep budgets and stopping rules for AI agents
Every agent needs to know when to stop. Budgets, loop detection, progress checks and graceful exits that keep agents useful and affordable.
AGENTSHuman-in-the-loop patterns that don’t annoy the human
Approve everything and people stop reading. Risk-based approvals, batching, good review screens and escalation patterns that keep humans in control.
AGENTSMemory for AI agents: what to store, what to forget
Working, episodic, semantic and procedural memory, and the rules for writing, retrieving, updating and deleting each without creeping users out.
AGENTSEvaluating AI agents end to end
Outcome checks, trajectory review, sandboxes, simulated users and pass^k. A complete method for knowing whether your agent really works.