Currently on the bench
Agent Buildprint is my current main project: executable contracts for coding agents — phase-flow packets, evidence ledgers, review loops, and replay gates instead of just prompt + spec.
Agents no longer start from a vague assignment. They bootstrap a selected-buildprint packet, read the phase-flow constitution, write schema-valid runtime evidence, and cannot sell blockers as success.
$ agb start → phase before code → evidence before trust → replay before done
AI Native Engineering
A self-paced learning path for developers who want to operate AI features, not just demo them — covering context budgets, Task Contracts, decomposition, evals, and fallbacks.
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Context windows, position effects, and lost-in-the-middle as real architecture constraints.
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Task Contracts, schemas, and source boundaries instead of longer prompts.
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Evals, traces, tool gates, and incident playbooks for operable AI features.
68 slides · self-paced · interactive
Open the full learning path with the interactive slide deck, context models, Task Contracts, and operable AI-feature patterns.
Open AI Native 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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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.
Read article →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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