Agent engineering series
How strong AI engineers build agents with datasets, scorecards, traces, and harness improvement loops.
How to Develop an Agent with an Agent Harness, End to End
An end-to-end field guide for building agents as measurable harnesses: context, planning, tools, records, evals, rollout, and learning.
How Great AI Engineers Build Agents: Datasets, Scores, and Harnesses That Improve
Why strong AI engineers build datasets, scorecards, traces, and improvement loops instead of treating agents as prompts plus tools.
Dataset-First Agent Engineering: The Golden Sets Behind Reliable Agents
A practical guide to golden sets, task distributions, corrected runs, held-out releases, and production slices for agent engineering.
Scorecards Over Vibes: The Five Metrics That Keep Agents Honest
The five metrics that keep agents honest: policy, utility, latency, safety, and economics.
Trace Review Is the Agent Debugger: Grade the Path, Not Just the Answer
How trace review grades the path, not just the answer, by inspecting context, plans, tools, guardrails, critic verdicts, and corrections.
Harness Candidates Are Model Checkpoints: How to Improve Agents Without Silent Mutation
How to treat every prompt, retrieval, tool, policy, and evaluator change as a scored, reviewed, reversible harness candidate.