Trustworthy Agent Memory and Context
AI agents do not only need bigger context windows or more remembered text.
They need context that can be trusted.
This guide is about the operating layer around an agent: GitHub, Slack, Linear, Jira, Sentry, docs, incidents, deployments, decisions, permissions, provenance, traces, and evals.
The problem is simple to state and hard to solve:
How does an agent know what is true, current, relevant, allowed, and safe to act on?
Start here
Section titled “Start here”Source
Section titled “Source”This guide is maintained in public:
Principles
Section titled “Principles”- Memory without provenance is a liability.
- Stale context is worse than missing context.
- Permissions are part of the memory model, not an integration detail.
- The system should make agents informed before it makes them autonomous.
- Durable memory should be easy for humans to inspect and correct.
- Claims about better agent behavior need evals, not vibes.
The current focus is software teams using AI agents and coding assistants.
That includes:
- source control, PRs, reviews, commits, and CI
- tickets, priorities, ownership, and acceptance criteria
- chat decisions and informal team knowledge
- docs, runbooks, ADRs, and onboarding notes
- monitoring, Sentry issues, incidents, and deploy history
- agent instructions such as AGENTS.md, CLAUDE.md, Cursor rules, and Codex skills
Authorship
Section titled “Authorship”Written by Hrvoje Pavlinovic. Maintained through HILLS Lab.