Everything You Need to Run AI Coding Agents
Compare the leading agents, map IDE + MCP integrations, and follow setup playbooks so your team ships safely with AI copilots.
Agent Resource Catalog
20 curated entries capture agent strengths, supported languages, pricing signals, and integration notes—filters + search are on the roadmap next.
AI Agents
Autonomous AI assistants and coding tools that help with various tasks
10 listings available
• Coding
• Writing
• Research
+ 1 more categories
Full browsing coming soon with category filters.
MCP Servers
Model Context Protocol servers providing contextual data to AI assistants
2 listings available
• Database
• File System
• API Integration
+ 2 more categories
Plugins/Extensions
Add-ons and integrations for various platforms and tools
10 listings available
• Claude Code Skills
• n8n Workflows
• IDE Extensions
+ 1 more categories
Full browsing coming soon with category filters.
Integration Recipes
Wire Agents Into Your Toolchain
Each recipe lists auth requirements, environment hints, and validation steps so you can go from prototype to production-ready automations quickly.
IDE & Editor Plugins
Wire agents into VS Code, JetBrains, Cursor, and Neovim to keep reviews and refactors lightning fast.
- Map agent commands to editor actions with readable prompts that include testing expectations.
- Store API keys in environment managers or secret stores—never in `.env.local` checked into git.
- Pin extension versions per environment so evaluations stay reproducible.
Model Context Protocol
Expose source maps, docsets, and build tools through MCP servers so agents can reason safely.
- Audit every MCP tool for read/write scope before enabling in production sandboxes.
- Ship health checks that confirm context providers respond in under 500 ms.
- Document fallback behavior so agents degrade gracefully if a server goes offline.
CLI & DevOps
Use agents inside terminals and CI runners for scaffolding, migrations, and release notes.
- Gate destructive commands behind confirmation prompts tied to branch protections.
- Log every agent-issued command so SRE teams can replay and audit activity.
- Run smoke tests after each agent-driven change before merging to main.
Runbooks & Guardrails
Best Practices for AI Coding Agents
Use these playbooks to align security, developer experience, and QA expectations before scaling agent-driven work.
Operational Guardrails
Codify roles, command budgets, and human-in-the-loop checkpoints.
- Define when agents may push commits or require reviewer sign-off.
- Track cost ceilings per workspace and set alerts when usage spikes.
- Mirror prod data? Mask secrets and PII with deterministic fixtures.
Collaboration Loops
Blend agents with engineers, QA, and PMs using structured prompts.
- Capture intent, constraints, and test plans up front in every task prompt.
- Rotate retrospective questions to hone prompting patterns weekly.
- Version control prompts/playbooks like code to share improvements.
Evaluation Playbooks
Measure agent output with regression tests, linters, and reviewers.
- Bundle evaluation suites in `tests/` and run them via `npm test` per change.
- Tag a11y and perf probes so you can isolate `npm run test:a11y|perf` quickly.
- Document remaining coverage gaps in PRs to keep transparency high.