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LLM-native Engineering
Production-grade LLM development with evals, observability, UX trust, cost, and ownership.
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The LLM-native developer needs more than prompts
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.
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blog
Your AI Agent Is Not Reflecting. It Is Defending Its First Answer
Asking one agent to reconsider its answer often produces a more confident defense of the same mistake. A bounded challenger-and-judge loop can create real alternatives, but only if disagreement, stopping, and judge bias are engineered explicitly.
blog
The Perfect Automated AI Eval Stack Does Not Exist
The reliable eval system is not one automated judge. It is a closed loop that combines portable traces, deterministic invariants, narrow semantic judges, versioned production failures, adversarial tests, and human calibration.
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Your Agent Needs an Operating Contract, Not a Bigger Prompt
The serious agent pattern is no longer bigger prompts and more encouragement. It is an operating contract: measurable goal, bounded tools, context sources, verifier evidence, review notes, rollback path, and a skill update when the run teaches you something.
blog
Stop Prompting Your Coding Agent. Give It a Loop.
The useful upgrade from prompt engineering is not a longer instruction block. It is a reusable loop spec: trigger, goal, allowed tools, verifier, terminal states, and memory rules. That is how repeated coding-agent work becomes operational instead of conversational.
blog
Better AI Products Need Systems, Not One Agent
Better AI products come from improvement systems around the agent. This guide shows how to build one with deterministic checks, narrow scoring rubrics, private holdouts, calibrated judges, and promotion gates.
blog
Agent Protocols Are Becoming a Stack, Not a Winner-Takes-All Standard
The useful question is not whether MCP, A2A, ACP, agents.json, Agora, ANP, LMOS, or AGNTCY wins. The useful question is which communication boundary you are designing: discovery, tool execution, task delegation, identity, transport, or runtime negotiation.
tools
Architecture Diagram MCP
Architecture Diagram MCP creates browser-ready architecture artifacts from agent briefs. It is built for platform maps, stakeholder explainers, and reproducible diagram outputs.
blog
Your Agent's Harness Is a Binary Now
Two 2026 papers from the same research lineage quietly retire prompt engineering as a discipline. The agent's system prompt is now a binary you can version, diff, and evolve with a 200-line loop. The four metrics that actually matter are not the ones your dashboard shows.
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AGENTS.md Is Not Context. It Is a Control Surface.
The surprising lesson from AGENTS.md benchmarks is not that context files are useless. It is that they change agent behavior, sometimes into more expensive and less useful work. Treat them as a control surface, not a repo manual.
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Put an AI Slop Gate After Tests and Lint
Tests tell you whether behavior still works. Linters tell you whether code is syntactically and stylistically acceptable. An AI-slop gate catches the residue coding agents leave behind: fake comments, swallowed errors, any-casts, duplicated helpers, TODO stubs, and dead code.
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Debug AI Reward Functions Like Production Incidents
Bad reward functions should not be treated like prompt drafts. Treat them like production incidents: preserve traces, classify the failure, patch only the implicated logic, and rerun against the same controls.
blog
Your AI-Built UI Needs a Playtester, Not a Screenshot Review
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.
blog
Stop Judging AI Code by the Diff
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.
blog
Agent harnesses should be specs, not hidden glue code
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.
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Spec-Driven Context Resets for Coding Agents
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.
blog
Agents Don’t Need Longer Prompts. They Need Harnesses.
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.
blog
Your Onboarding Is Why Your Team Is Vibe Coding
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.
blog
AGENTS.md is not enough: your coding agent needs a harness
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.
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Prompt Decomposition: How to Break Down AI Tasks Properly
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.
blog
The LLM-native developer needs more than prompts
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.
blog
Prompting Is Dead. Context Wins.
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.
blog
AI-first Architecture: Faster Decisions, Still in Control
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.