Agent engineering series
How strong AI engineers build agents with datasets, scorecards, traces, and harness improvement loops.
OpenWorker Review: A Real Desktop Coworker With an Unfinished Trust Runtime
OpenWorker already owns the agent loop, approvals, connectors, and desktop UX. Its next leap is containment, durable effects, replay, budgets, and evals.
Graph Engineering for AI Agents: The X Hype Cycle and a Production Playbook
Separate graph-engineering signal from X hype, then compile agent workflows with governed transitions, graph invariants, durable effects, and exact replay.
Reverse Prompting: Work Backward from the Output, Then Prove the Prompt
Reverse prompting is useful when it turns a desired output into a candidate instruction contract and validates that contract forward. Here is the research, the workflow, the failure modes, and the security boundary.
Passive Awareness Is the Missing Multi-Agent Primitive—But It Needs a Control Plane
AgentRadio shows that agents improve when discoveries can cross lane boundaries during execution. Production teams should keep the asynchronous channel, then add typed evidence, bounded delivery, authority isolation, and replay.
Your Agent Benchmark Is Measuring the Harness
Two new evaluation systems separate benchmark, harness, model, and environment. Their results show why one leaderboard score cannot tell you which agent to ship.
Multi-Agent Systems Need Replay Before More Agents
Multi-agent systems multiply coordination, cost, and causal ambiguity. Prove one agent is replayable before adding lanes and handoffs.
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.