Last reviewed: September 2026
Intent engineering is the practice of designing an agent around what users are trying to accomplish, not only around prompts, tools or isolated messages. It requires durable context, but the operating input is production evidence.
A message is not an intent
"Can you check that again?" is almost meaningless without the earlier turns. The user may be challenging a refund decision, asking a coding agent to rerun tests, or correcting a research answer. Intent lives across the session.
That is why intent analysis needs stable session and user identifiers. A classifier that sees only one message will confuse corrections, follow-ups and retries with new tasks.
Build a taxonomy from real work
Start with the workflows users actually attempt. Group complete sessions, inspect examples, and name categories at the level where a product decision is possible. "Billing" is often too broad. "Explain an invoice charge" and "request a refund" may use different tools, have different outcomes and fail for different reasons.
A useful intent taxonomy should be:
- Mutually understandable, even when categories overlap at the edges
- Stable enough to compare over time
- Specific enough to map to an owner or capability
- Connected to outcomes and evidence sessions
- Versioned when definitions change
Measure the full path
Intent volume alone tells you what users ask for. It does not tell you whether the agent helps them. For each intent, examine issue-free sessions, explicit feedback, abandonment, repeated attempts, tool paths and measurable outcomes.
For MCP workflows, reconstruct the path from use case to tool calls to result. A popular intent with a long looping path is a different product problem from an intent the agent cannot serve at all.
Keep the evidence reviewable
Every cluster should link back to representative sessions. This protects the team from trusting a label that has drifted away from reality. It also gives product, support and engineering the same source material when deciding what to change.
Flowlines organizes production sessions by intents, users, outcomes and recurring issues while preserving the path to the original conversation and tool calls. It does not turn intent into a static dashboard category. The taxonomy remains an operating model that can be checked against current behavior.
See the production product or learn how user cohorts are analyzed.