Context Management
Connectors and retrieval policies that assemble the right live context per turn—freezes, incidents, scans, and catalog state
Exemplar
How this harness capability fits the Exemplar platform—governed agent operations, not a standalone prompt playground.
Why Exemplar
Stale or missing context produces confident wrong actions; dumping everything into the window burns budget without improving decisions.
Context engineering in Exemplar is retrieval plus policy: what an agent may see, when, and from which systems of record.
What Exemplar delivers
Context Lake connectors keep agents aligned with production—same graph as the dashboard and MCP tools.
Turn-level assembly: incidents, policy state, fresh scan results, and catalog resolution before any tool call.
How teams use it
Enable connectors for the systems your agents need; map signals to catalog components and policies.
Define context budgets per workflow; compaction rules drop noise while preserving decision-critical fields.
Capability checklist
Developer guide
Official documentation on docs.exemplar.dev for this capability.
Open developer guide (opens in a new tab)Contact sales
Harness Platform is scoped per deployment. Talk to us about this feature.
From the blog
Related posts on exemplar.dev.
- Moving from prompt and context engineering toward harness engineering
Three layers behind production agents: shaping the ask, assembling the window, and building the runtime loop. Where each discipline stops and what to invest in next.
- Agents, context, and guardrails on a unified platform
From code completion to production actions; Context Lake, catalog, governance, and Agentic Assistant/MCP for safe automation.
- Knowledge Architecture for AI Coding Agents: Beyond the AGENTS.md File
How to structure what your AI coding agent knows — the docs directory, context boundaries, ownership, versioning, and the difference between a knowledge system that stays useful and one that silently rots.