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AI agents at work

An AI agent is only useful when it can act on real work with real guardrails: a scoped role, the right permissions, durable memory, and a human approval gate where it matters. Without that frame, “agentic” mostly means an unsupervised script with a chat interface.

These guides cover how to define agent roles, how to give agents the context they need without giving away the workspace, and how documentation becomes the memory that makes agents reliable over time.

Read the agents guide in the docs
Can AI agents run a sprint? Six project management tools compared
Featuredresearchaiagentsproject-managementcomparison

Can AI agents run a sprint? Six project management tools compared

Compare the documented agent capabilities of Linear, ClickUp, Notion, Asana, monday, and Stellary across one reproducible sprint benchmark.

11 min read
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An AI-ready project brief: the template that stops endless prompting
aiguideproject-managementagents

An AI-ready project brief: the template that stops endless prompting

An AI project brief connects outcomes, scope, sources, constraints, and validation. Use this template before assigning work to an agent.

6 min read
How to coordinate multiple AI coding agents without collisions
aiagentsdevelopmentguide

How to coordinate multiple AI coding agents without collisions

Task boundaries, context, worktrees, contracts, and integration: a method for running multiple AI coding agents in parallel without multiplying conflicts.

6 min read
GPT-6 Astra vs Claude Fable 5.1: which model should you use for coding?
aiagentsopenaianthropiccomparison

GPT-6 Astra vs Claude Fable 5.1: which model should you use for coding?

Astra or Fable 5.1? Compare capabilities, pricing, context, safeguards, and agentic workflows to choose the right coding model.

11 min read
Your AI agent says “done”: how to check its work
aiagentsguide

Your AI agent says “done”: how to check its work

Your AI agent says the job is done. Learn how to check the actual result, find supporting evidence and catch missing work before you sign it off.

4 min read
Local, cloud, or hybrid AI: how to choose
aiagentslocal-aiprivacyguide

Local, cloud, or hybrid AI: how to choose

A simple guide to choosing local, cloud, or hybrid AI based on your documents, budget, internet connection, and everyday needs.

8 min read
Claude Fable 5.1 vs Opus 5: which model should your AI agents use?
aiagentsanthropicguide

Claude Fable 5.1 vs Opus 5: which model should your AI agents use?

Fable 5.1 or Opus 5? Choose by mission difficulty and cost per successful task — not by putting Anthropic’s strongest model everywhere.

10 min read
All articles (17)
  1. Can AI agents run a sprint? Six project management tools compared
  2. An AI-ready project brief: the template that stops endless prompting
  3. How to coordinate multiple AI coding agents without collisions
  4. GPT-6 Astra vs Claude Fable 5.1: which model should you use for coding?
  5. Your AI agent says “done”: how to check its work
  6. Local, cloud, or hybrid AI: how to choose
  7. Claude Fable 5.1 vs Opus 5: which model should your AI agents use?
  8. GPT-5.6 Sol, Terra, and Luna: which OpenAI model should your AI agents use?
  9. MCP vs REST API: which architecture should you choose for AI agents?
  10. AI agent, AI workflow, or automation: which should you choose in 2026?
  11. AI Model Fusion: Why the Future Is Not One Model, but a Multi-Agent Architecture
  12. The Future of AI Agents Is Orchestration, Not Intelligence
  13. AI Scrum Master: What It Can Do and What It Cannot
  14. How to Manage AI Agents in Project Management Without Losing Control
  15. Documentation and AI Memory: Why Your Agents Should Not Start From Zero
  16. How to Connect Docs, Delivery, and AI Agents in One Workflow
  17. Project Management with AI Agents in 2026