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Field notes on running agents.

How we build, secure, and run a fleet of self-hosted AI agents.

Engineering

AI Coding Agent Over-Editing and Diff Size Limits

AI coding agent over-editing is a reviewability failure, not a style annoyance. The agent touches more files than the task needs, bundles behavior with…

Engineering

Coding Agent Action Bias and No Code Change Needed

The best coding-agent result is sometimes no diff. That sounds obvious until you measure it. FixedBench, a 2026 benchmark built from 200 already-fixed…

Engineering

How to Prevent AI Coding Agents Modifying Tests

If an agent can rewrite the verifier, a green build stops meaning what your team thinks it means. The practical way to prevent AI coding agents modifying tests…

Engineering

Claude Code LSP Setup for Production Repositories

The useful version of Claude Code LSP setup is not "turn on code intelligence and trust it." For a production repo, it is a small platform rollout: install the…

Engineering

AGENTS.md vs CLAUDE.md vs Cursor rules for agent teams

<article> <p>A platform team adds Codex to one repo. A frontend team keeps using Cursor. A backend team standardizes on Claude Code. Within a…

Engineering

Claude Code plan mode workflow for team approval gates

<article> <p>A senior engineer asks Claude Code to change a deployment workflow. The agent understands the request, finds the YAML file, edits a…

Engineering

Playwright CLI vs MCP for Claude Code in production

<article> <header> <p>Claude Code can drive a browser in more than one way. That is useful, but it also creates a decision that teams should…

Engineering

Claude Code checkpoints vs Git commits for team workflows

<article> <p>You ask Claude Code to clean up a tangled module. It touches five files, runs a formatter, tries a test command, changes direction…

Engineering

Claude Code monorepo setup for large engineering teams

<article> <p>A monorepo team usually notices the problem in a small, annoying way. Claude Code starts confidently, reads a few files, then reaches…

Engineering

AI coding agent database migration safety model

AI coding agent database migration safety starts with a simple question: what can the agent actually touch? For a platform or security engineer, the decision…

Engineering

Claude Code skills vs subagents vs MCP for teams

Choosing between Claude Code skills vs subagents vs MCP is not a question of which feature is most powerful. For a senior engineer designing a team workflow,…

Engineering

AI Coding Agent PR Reviewability Checklist for Teams

AI coding agents make pull requests cheaper to create. They do not make reviewer attention cheaper. For engineering managers and senior engineers, the…

Engineering

Claude Code subagents best practices for team governance

Claude Code subagents best practices matter most when a team moves from individual experimentation to shared automation. A subagent can keep noisy research,…

Engineering

Claude Code compaction strategy for long coding tasks

A good Claude Code compaction strategy treats compaction as a handoff boundary, not as durable memory. That distinction matters when a coding task spans hours,…

Engineering

Self-hosted Coding Agent Runtime Comparison Guide

The right self-hosted coding agent runtime is the one that matches your trust boundary. For most security-conscious teams, that means per-task isolated…

Engineering

Persistent Memory for AI Coding Agents: Governance

Persistent memory for AI coding agents should be treated as platform infrastructure, not as a bigger prompt. The useful design is layered: small repo…

Engineering

MCP Context Bloat: How to Govern Tool Growth

MCP context bloat is the hidden tax of giving coding agents too many tools at once. Each enabled MCP server can add tool names, descriptions, input schemas,…

Engineering

Claude Code Worktree Database Isolation Guide

Claude Code worktree database isolation means giving every agent its own runtime namespace as well as its own checkout. Git worktrees and Claude Code's…

Engineering

AI Coding Agent Secrets: Protect .env and Tokens

AI coding agent secrets should not rely on .env files, ignore rules, or tool permissions as the main boundary. The workable pattern is layered containment:…

Engineering

Multi-Agent Coding Orchestration Is Ops Now

Multi-agent coding orchestration works when agents run in parallel but review, isolation, approvals, and merge decisions stay controlled.

Engineering

Coding Agent Dependency Security: Privileged Installs

Coding agent dependency security starts by treating package installs as privileged operations, with policy approval, lockfiles, and registry controls.

Engineering

AGENTS.md Best Practices for Coding Agents

AGENTS.md best practices for coding agents: what to include, what to leave out, how to handle monorepos, and where enforcement belongs.

Engineering

Claude Code Hooks for Permissions: A Safer Pattern

Claude Code hooks for permissions can reduce approval fatigue when paired with narrow allowlists, sandboxing, managed settings, and audit logs.

Engineering

MCP Server Monitoring: What Reliability Requires

MCP server monitoring needs more than uptime checks. Track tool discovery, auth, latency, retries, timeouts, and per-tool safety controls.

Engineering

Cloud Coding Agent vs Local CLI Agent: Runtime Guide

Cloud coding agent vs local CLI agent is really a runtime decision: compare ownership, security, secrets, persistence, cost, and PR handoff before rollout.

Engineering

Coding Agent Evaluation Metrics for Real Repos

Coding agent evaluation metrics should measure repo tasks, review quality, cost, regressions, partial progress, and oversight beyond leaderboard resolve rate.

Engineering

Coding Agent Cost Monitoring: Control Spend Early

Coding agent cost monitoring helps platform teams control token, credit, and workflow spend by combining budgets, telemetry, attribution, and policy.

Engineering

Securing Coding Agents in CI/CD: Practical Baseline

Securing coding agents in CI/CD starts with one rule: treat untrusted GitHub content as hostile, then limit secrets, tokens, network, and write access.

Engineering

AI Code Review Agent: Evaluation Guide for PRs

An AI code review agent should be evaluated as a governed PR workflow, not a comment bot. Compare signal, cost, permissions, context, and human review impact.

Engineering

Credential Proxy for AI Agents Without Secret Exposure

A credential proxy for AI agents lets agents call private repos, APIs, CLIs, and MCP tools without exposing raw secrets to runtimes, prompts, or logs.

Engineering

Agent Harness for Coding Agents: Runtime Architecture

An agent harness for coding agents controls sandboxes, state, permissions, tool execution, review flow, lifecycle, and cleanup around safe AI coding work.

Engineering

AI agent observability for safe coding agent rollouts

AI agent observability for coding agents: trace runs, commands, diffs, tests, costs, approvals, and risky side effects before code reaches production.

Engineering

Long-running background AI agents need durable workers

Long-running background AI agents need durable workers, queues, checkpoints, approvals, sandboxing, cost caps, observability, PR review, and control.

Engineering

Persistent AI Agent Workspace Architecture Guide

Design a persistent AI agent workspace with durable files, sandbox snapshots, memory boundaries, rollback, tenant isolation, and clear retention policy.

Engineering

Self-hosted coding agent runtime: build, buy, operate

A self-hosted coding agent runtime gives policy control and data residency, but shifts sandboxing, secrets, audit, cleanup, and capacity to your team.

Engineering

Production Workflows for AI Coding Agents That Scale

Production workflows for AI coding agents need isolated workspaces, reviewable diffs, CI controls, protected merge gates, and accountable human ownership.

Engineering

MCP vs function calling: Practical architecture guide

MCP vs function calling explained for engineers choosing between direct tool calls, MCP servers, runtime discovery, auth boundaries, latency, and reuse.

Engineering

MCP Security for AI Agents: Production Controls

MCP security for AI agents needs token audience checks, sandboxed tools, schema pinning, approval UX, egress limits, and clear incident-ready audit trails.

Engineering

AI Agent Sandboxing: Secure Coding Agent Controls

AI agent sandboxing helps security leaders control source access, secrets, network egress, and coding agent execution risk before agents touch private code.