This guide is vendor-neutral on the harness but grounded in a real system. Where a
practice maps onto a concrete capability, we point at how Open Index
implements it — Open Index is the open-source context engine we extracted from doing
this work at scale.
The two halves of an agent
An autonomous agent is a loop over two things:- A harness — the control flow, gates, retries, sub-agents, and evals that keep the loop honest. See The harness.
- A context layer — how the agent finds the right knowledge, keeps it current, and cites it. This is where the rest of this guide focuses.
What breaks, and where it’s covered
Context poisoning
The agent picks up unrelated instructions and drifts. Fix it at the data model,
not with more folders.
Context search accuracy
Retrieval that returns plausible-but-wrong context. Make discovery the agent’s
job, with navigation.
Conflicts & staleness
Knowledge that self-contradicts as it grows. Structure updates per concept.
Provenance
Users can’t trust what they can’t trace. Attach a source to every fact.
Human steering
The autonomy tax. What steering is, why it happens, and how to reduce it.
Determinism
Same input, different output. Where to push variance out of the loop.
Self-learning loops
Agents that get better with use by writing back what they learn.
The harness
Soft gates, critic sub-agents, and in-session evals around the context layer.
The one idea underneath all of it
If there’s a single thread through this guide, it’s this:Don’t feed your agent context. Give it structured, searchable, attributable context and make discovery its job.Everything else — less poisoning, less steering, more determinism, real provenance — follows from that shift. The next pages break it down failure mode by failure mode.