13 docs tagged with "agents"

Choose what an AI assistant should remember and retrieve, without confusing stored notes, search results, and authority.

Untrusted content will influence the model, so the execution system must limit what that influence can achieve: what prompt injection, permissions and approvals, sandboxes, credentials, and MCP authorization each cover.

Store, connect, and retrieve Markdown notes across conversations while keeping recorded decisions separate from permissions.

How step limits, tool validation, verification, cancellation, and durable state keep agent loops useful and recoverable.

Add a tool, a Skill or an MCP server to the agent you use, or write your own loop and keep long runs safe.

How to assemble a small, current working set while preserving provenance and durable state outside the model window.

Let AI find gaps, explain one step, and provide practice while keeping recall and problem solving with the learner.

Recover an agent’s progress after interruption without duplicating the side effects of retries.

When work is split across agents, how to separate context, working directories, and permissions, who integrates and verifies the result, and what an event log can recover.

Find Markdown notes with keyword, semantic, or hybrid search, then read the source behind each result.

Use a two-state example to distinguish immediate rewards, long-term value, exploration, and learned agent policies.

Design tools whose inputs, authority, side effects, failures, and evidence remain legible to models and operators.

Combine semantic discovery and keyword search to locate relevant workspace files, then verify the current source.