Questions / Production agents
Straight answers on agents in production.
The questions builders and product teams bring to behavioral observability, each answered with production context and a concrete example.
start with the question, not the dashboard
How do I connect Flowlines to my agent?
Connect Flowlines to Claude Code or Codex with the Use with button. Then choose what to monitor: your MCP server, an application agent, or your coding tool. Each has its own setup and data boundary.
Do I need to install an SDK?
It depends on the data path. Existing application-agent traces can be connected without a proprietary Flowlines SDK. MCP server observability requires an OpenTelemetry-compatible server integration.
How long until I see signal?
A session can appear after Flowlines accepts or imports its trace data. Recurring issue rates, cohort comparisons and trend deltas become reliable only after the selected scope contains enough analyzed sessions, and the product labels insufficient samples instead of presenting a misleading rate.
Which LLMs and frameworks does Flowlines support?
Flowlines is model and framework agnostic when your stack emits compatible OpenTelemetry traces or stores runs in Langfuse or LangSmith. The ingest path understands standard OTLP semantics and gen_ai.* attributes for model, token and prompt context.
How do I know if my AI agent is working in production?
Flowlines answers this with analyzed chats, active users, issue-free rate, negative feedback, recurring intents, issues, and sessions that need attention. Open an issue to review the affected users, use cases, and production sessions.
How do I detect agent drift over time?
Agent drift is a recurring change in behavior that becomes visible across sessions, users, intents, or tool journeys. Flowlines groups drift-related issues and keeps the exact sessions attached, so teams can distinguish a pattern from a one-off anomaly.
How do I find the failure modes my logs don't show?
The failures that matter most are often behavioral, not exceptions: a successful response can still be wrong, looping, abandoned, or off-policy. Flowlines separates behavior issues from reliability errors and opens the affected production sessions.
What AI agent issues does Flowlines surface?
Flowlines separates behavior issues from reliability errors. Behavior categories include looping and stuck behavior, drifting, user frustration, abandonment, result quality, hallucination and trust, and safety and policy.
What do my users actually want from my agent?
Users describe what they want in conversations, even when they never submit formal feedback. Flowlines groups analyzed sessions by recurring intent and connects those intents to users, issues, and outcomes.
How do I tell when users are frustrated with my agent?
Frustration can appear as repeated corrections, rephrased requests, negative feedback, or abandonment. Flowlines surfaces user frustration as a behavior issue and opens the affected users and production sessions.
How do I measure user satisfaction without CSAT surveys?
Behavioral analysis does not replace a CSAT survey, but it fills in what surveys miss. Flowlines combines negative feedback, issue-free sessions, repeat usage, activity depth, and affected user groups across analyzed production sessions.
How do I segment my agent's users by behavior?
Flowlines segments observed users with activity and issue evidence, then lets teams create groups using customer, model, environment, or another useful trace property. The Users view shows repeat usage, activation depth, issue coverage, cost, and execution time without inventing a black-box churn score.
Why are users abandoning my agent?
Users often abandon after an unanswered intent, a poor result, a loop, or a slow journey that infrastructure logs still call successful. Flowlines surfaces abandonment as a behavior issue and keeps the affected users and production sessions attached.
How do I find feature requests buried in conversations?
Feature requests often appear inside ordinary agent conversations. Flowlines gives product teams recurring intent groups and the underlying sessions, which provides a practical place to find unmet needs without treating the product as an automatic feature-request tracker.
Why isn't running evals enough?
Evals score the criteria and populations you define. Offline and online evals are essential, but they can still miss recurring production behavior outside those criteria. Cross-session analysis adds changing intents, affected users, drift, and patterns that only emerge at scale.
Is my new prompt working better than the old one?
Compare real production sessions that carry a prompt or release identifier. Flowlines Groups can compare sessions by model, environment, customer, or another useful trace property, but the current product does not invent an automatic causal deployment score.
How do I catch silent failures in production?
Define a successful user outcome, compare it with the tool result, and review sessions where completion claims lack evidence. Monitor this pattern over time and route recurring failures to an owner.
How do I know what to fix next?
Start with the issue affecting the most important users or sessions, then verify it against the attached evidence. Flowlines gives each issue an impact context, an explanation, a next step, technical detection details, and the exact sessions needed to decide whether the fix belongs in the prompt, tool path, retry policy, or product.
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