13 docs tagged with "ai-systems"

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

How to package a repeatable agent procedure, test its trigger and outcome, and manage portability and code-execution risk.

A practical map of the control, context, tool, evidence, and evaluation layers around AI agents.

Understand coding-agent execution, recovery, and acceptance through harness responsibilities, Pi's minimal core, and its safety boundaries.

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

Understand production agent harness responsibilities through Codex runtime state, context compaction, integration surfaces, and two-layer security controls.

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

A practical way to label specifications, vendor claims, benchmark results, observations, and recommendations without pretending they prove the same thing.

Separate capture, evidence, private understanding, and public writing without building another central knowledge base.

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

A protocol-level view of MCP architecture, lifecycle, primitives, transports, trust boundaries, and alternatives.

How classical software engineering principles—design concepts, ubiquitous language, TDD, deep modules, and gray-box delegation—resolve failure modes in AI-assisted coding.

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