What context observability is
Context observability is the ability to measure, continuously, the quality of the knowledge agents consume — not just whether the system is up, but whether the delivered context is sufficient, current and relevant. It is what turns "guesswork about quality" into data-driven decisions.
While traditional observability looks at runtime latency and errors, context observability looks at the source of the answers: where knowledge is missing, which sources support what and where the agent is blind.
What Chatydata measures
The focus is making context quality actionable. Each indicator points to a concrete improvement in the knowledge base.
Context quality score
A consolidated indicator of coverage, freshness and consistency across collections.
Knowledge gaps
Topics for which there is no trusted source for the agent to answer from.
Unanswered questions
What users ask and the context does not cover — a prioritization queue of what to document.
Most-used sources
Which documents support most answers and which can be reviewed or retired.
Context drift
Change in query patterns and sources over time, signaling staleness.
From gaps to actions
The value of observability is not in showing a number, but in closing the loop. When the dashboard reveals that many questions on a topic go without a trusted answer, that becomes a clear task: document the source, approve the content and update the collection.
This loop — measure, identify gap, feed context, measure again — is what makes the knowledge base improve steadily instead of degrading over time.
Where observability acts
Observability feeds off context delivery and the audit trail. Every retrieval that passes through the governance layer generates signals that feed the metrics, without instrumenting each runtime separately.
Fontes
Drive, SharePoint, ERP, CRM, PDFs, APIs
Chatydata · Context Engine
Organiza · versiona · governa · observa o contexto
Runtimes
via MCP · API · conectores · pipelines
Risks of operating agents blind
Without observability, context quality only surfaces when someone complains about a wrong answer — and even then, without data, it is hard to act. The risks:
- Silent degradation. Context ages and quality drops with no signal until the problem erupts.
- Invisible gaps. The company does not know what the agent cannot answer well.
- Misdirected effort. Without data, the team documents what it thinks is important, not what users actually ask for.
- Persistent bad sources. Problematic documents keep supporting answers without anyone noticing.
How to start
Observability is enabled together with context delivery: as soon as agents start consuming governed collections, the signals begin feeding the metrics. The first win is usually the list of unanswered questions, which immediately guides what to document.
The readiness assessment already anticipates where gaps are likely to exist, shortening the time to the first improvements.
Frequently asked questions
Do I need to instrument my runtime to get observability?
No. The metrics come from the context delivery and audit layer. Because signals are collected at retrieval, you do not need to instrument each runtime separately.
What is the context quality score?
A consolidated indicator of the health of your collections, combining coverage, freshness and source consistency. It is used to track the evolution of the base over time.
How do unanswered questions help?
They show exactly where users need something the context does not yet cover, turning base improvement into a queue prioritized by real demand.
What is context drift?
It is the change over time in query patterns and source relevance. Detecting drift helps you notice when a collection is stale before it becomes a wrong answer.
Free assessment: we identify gaps and fragile sources in your current base.
Assess the context quality of your agents