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AI PLAYBOOKS

Less jargon.
More “here’s how.”

A practical path from one useful idea to a controlled, tested workflow. Start with the step you need and use the guides and tools below.

01

Start with one useful job

Describe the workflow, the people it affects and a result you can check. Identify assumptions before choosing tools.

Shape an idea with App Builder →
05

Test the result

Use made-up data. Verify allowed actions, rejected requests, retries and recovery. Compare actual results with the approved outcome.

Use the agent-approval checklist →

Understand the building blocks.

Open a topic for a plain-language explanation and a reference. These are starting points; your implementation still needs its own checks.

AGENTS.md & SKILL.md

Project instructions describe how work should be done. Skills package instructions and resources for a particular task. Confirm how your chosen tool discovers and applies them.

Front matter

A short metadata block at the beginning of a Markdown file. It can name a skill and explain when it applies. The required fields depend on the tool reading it.

MCP & A2A

MCP helps connect AI applications to tools and context. Agent-to-agent communication (A2A) concerns agents working across systems. Neither replaces your own authorization and approval checks.

Security & human review

Define data boundaries, least-privilege access, credential handling, approvals, logging and recovery. Verify these controls in the running application.

Need a technical partner to put this into practice? Talk with AEVUS about a scoped implementation →