Agent Wiki Topic
Agent Security
Secure agent runtimes, tool gates, prompt-injection controls, and auditable permissions.
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Audit Local LLM Agents Like Runtimes
Local LLM agents can touch shells, files, browsers, credentials, memory, and messaging tools. Treat their runtime layer as source code worth auditing, then turn static findings into a manual review queue instead of automatic verdicts.
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Coding Agents Need Hardened Harness Evals
Permissive coding-agent benchmarks hide a boring production truth: security policy changes agent behavior. Small teams should run the same task suite under nested hardening levels and separate model failures from tasks the policy made impossible.
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Harness Engineering Is Systems Engineering for AI Agents
Harness engineering is the work of turning a probabilistic model into a controlled system. This field guide maps the twelve engineering surfaces around the model and shows how to evaluate and evolve them.
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Your AI Agent Learned Something. Should It Be Allowed to Remember It?
An agent that writes a lesson into memory, a skill, a prompt, or its own code is deploying behavior into future runs. This guide shows how to put persistent changes through evidence, eval, approval, expiry, and rollback gates.
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Your Coding Agent Can Be Tricked by Boring Shell Commands
The MOSAIC paper shifts the coding-agent security question from hostile prompts to command traces. The practical move is to audit producer-consumer state across shell commands before generated state crosses into privileged work.
blog
Audit Local LLM Agents Like Runtimes
Local LLM agents can touch shells, files, browsers, credentials, memory, and messaging tools. Treat their runtime layer as source code worth auditing, then turn static findings into a manual review queue instead of automatic verdicts.
tools
Is This Agentic Change Good?
This skill reviews branches that change future agent behavior. It collects local git evidence and judges skill collisions, context budget, safety risks, and runtime visibility before recommending a merge.
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The Next Prompt Is Not a Prompt. It’s a Workflow.
Dynamic workflows move agent work from one chat prompt into inspectable orchestration: phases, subagents, evidence, budget, permissions, adversarial review, and stop conditions. The point is not more agents. The point is better control.
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AI Agents Need Evidence Before They Click
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.
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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.
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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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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.