The Problem Contexity Solves
AI coding agents routinely lose or misuse context in ways that cost real time and produce unreliable results:- They repeat full repo discovery at the start of every session.
- They forget prior investigations the moment a session ends.
- They reuse stale notes after files move or behavior changes.
- They cannot explain where a remembered fact originally came from.
- They cannot demonstrate whether stored context actually saved any work.
What Contexity Gives Agents
When you attach Contexity to a project, agents gain access to a set of capabilities they cannot build for themselves mid-task:- Project identity and state — a stable, persistent identity for the project that survives session boundaries and team handoffs.
- Task-aware context packs — bounded bundles of relevant context assembled for a specific task, not an unbounded dump of everything.
- Source-backed project intelligence — project structure, dependency relationships, and behavioral patterns derived directly from source, not from agent memory alone.
- Stale context suppression — entries that no longer reflect reality are filtered out before the agent ever sees them, so agents do not act on outdated assumptions.
- Candidate-first memory writes — agent-proposed memory additions enter a candidate state and require confirmation before they are treated as trusted context.
- Run ledgers and closeout checkpoints — every agent run is tracked from start to finish, giving you an auditable record of what the agent read and what it changed.
- Visible metrics when enabled — opt-in heuristic metrics surface estimated context reuse and session savings, useful for evaluating how much work Contexity is doing across your projects.
- External source capture — Contexity can ingest context from Slack threads, issue comments, documentation, external repositories, and product direction documents, so agents are not limited to what they can read from source files.