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Practical writing about agents, automation, and software architecture.
Detected Focus
Several recent posts point to the same engineering problem: agents do not only need better prompts. They need a runtime contract around the prompt.
The minimal model
A useful harness defines the agent’s phase, allowed actions, required evidence, exit condition, and blocker rules. That is the difference between an AI that tries to help and an agent that can be operated.
An agent harness is the control layer around an AI agent. It defines allowed actions, state transitions, required evidence, and stop conditions.
Without a harness, the model decides too much from chat context. It may edit before inspecting, mark work done without running checks, keep looping after the useful work is finished, or create fake review comments because the prompt asked it to criticize.
The practical fix is to move those rules out of vibes and into an explicit contract: phase state controls which actions are allowed; evidence gates control when the agent may claim progress; exit conditions control when the loop stops; blocker rules control when the agent must ask for help; review rules prevent pointless self-critique.
Related reading
Natural-Language Agent Harnesses give a useful name to an important shift: the agent policy should be an inspectable document that a runtime executes, not invisible glue hidden inside controller code.
When an agent keeps jumping from planning to editing to testing at the wrong time, the fix is not usually another paragraph of system prompt. Put the workflow into explicit states, give each state a tiny tool policy, and make phase changes visible.
When an agent clicks, sends, pays, deletes, or extracts data, the critical truth cannot live only in model prose. Put a small evidence gate before risky tool calls: predicate, evidence type, source, decision.
The useful lesson behind Claude Code /goal is not that agents can run forever. It is that long-running agent work needs an explicit, observable exit condition: what proves done, what stays in scope, and when to stop blocked.
Open-ended instructions like “critically self-check this” accidentally reward the model for producing criticism. The fix is not less review. It is calibrated review: explicit criteria, PASS_NO_CHANGE, evidence per finding, severity thresholds, and a tiny change budget.
May 28, 2026 · AI-first Engineering
AI-generated interfaces often look finished before they behave correctly. A GUI playtester loop uses a separate browser agent to interact with the artifact, record screenshots and action logs, turn broken flows into reproducible bug reports, and rerun the same script after repairs.
May 28, 2026 · AI-first Engineering
Better AI coding is not mainly about better prompts. It is about the harness around the model: explicit contracts, separate builder and reviewer roles, evidence requirements, and a loop that turns failures into better specifications.
May 26, 2026 · AI Agent Workflows
The useful lesson behind Claude Code /goal is not that agents can run forever. It is that long-running agent work needs an explicit, observable exit condition: what proves done, what stays in scope, and when to stop blocked.
May 26, 2026 · AI Agent Workflows
Agent evals should not only ask whether the final answer looked good. A useful benchmark measures the whole agent system: skill routing, tool policy, evidence, outcomes, hard-fail safety cases, regressions, cost, and production drift.
May 25, 2026 · AI Agent Workflows
When an agent keeps jumping from planning to editing to testing at the wrong time, the fix is not usually another paragraph of system prompt. Put the workflow into explicit states, give each state a tiny tool policy, and make phase changes visible.
May 24, 2026 · AI-first Engineering
Natural-Language Agent Harnesses give a useful name to an important shift: the agent policy should be an inspectable document that a runtime executes, not invisible glue hidden inside controller code.
May 23, 2026 · AI-first Engineering
Long agent chats rot. A better pattern is to move decisions into small spec files, clear context between layers, and let each coding-agent session read only the artifact it needs.
May 21, 2026 · AI Agent Workflows
When an agent clicks, sends, pays, deletes, or extracts data, the critical truth cannot live only in model prose. Put a small evidence gate before risky tool calls: predicate, evidence type, source, decision.
May 21, 2026 · AI Agent Workflows
Open-ended instructions like “critically self-check this” accidentally reward the model for producing criticism. The fix is not less review. It is calibrated review: explicit criteria, PASS_NO_CHANGE, evidence per finding, severity thresholds, and a tiny change budget.
May 20, 2026 · AI-first Engineering
The arXiv survey Code as Agent Harness names the next shift in agent engineering: code is not only what agents generate. It is becoming the executable, inspectable, stateful runtime that makes agents reliable.
May 20, 2026 · AI-first Engineering
Teams do not usually start vibe coding because developers became careless. They start because onboarding is broken: docs are stale, harnesses are undocumented, system knowledge lives in people’s heads, and AI turns missing context into plausible code and Markdown.
May 19, 2026 · AI-first Engineering
A coding agent is not made reliable by one magic prompt. It needs a harness: AGENTS.md, skills, tool permissions, hooks, and evals that catch behavior drift.
May 19, 2026 · AI Agent Workflows
The useful move is not one mega assistant for all client work. Give each client project a small, isolated agent with its own memory, tasks, preview URL habit, and boring daily standup.
May 18, 2026 · AI-first Engineering
After context engineering comes decomposition: developers should stop putting everything into one prompt and instead split tasks into direct prompts, subtasks, pipelines, agent loops, or skills.
May 15, 2026 · AI-first Engineering
The next developer skill is not writing clever prompts. It is building the operating system around LLMs: data quality, model versioning, evals, guardrails, incident response, review UX, and repo instructions agents can actually follow.
May 15, 2026 · Personal AI Workflows
Voice is not good for everything. But for small agent jobs it is brutally useful: dictate a task while moving, transcribe it locally, let your existing agent handle it, and get only a short answer back.
May 12, 2026 · AI-first Engineering
In 2026, good prompting is not about one magic sentence. The better approach is to curate context, define tools and schemas, set agent rules, and verify behavior with evals.
May 11, 2026 · AI Agent Workflows
Hermes gets interesting when an agent does not only produce output, but reviews the run: execute, measure, critique, rewrite the skill, and test again. The loop pays off mainly for repeatable workflows.
April 29, 2026 · AI-first Engineering
AI-first architecture does not mean the model decides. It means AI generates options, finds risks, compresses context, and the team makes a traceable decision.
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