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Overview

Building conversational AI agents is an exercise in iteration. With stochastic systems, confidence is not earned on day one - it is curated over time across two loops. At Quraite, this process is called the Curation Loop: how teams move from:
“The agent mostly works” to “We are confident in this agent in production.”

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Why the Curation Loop Exists

Most agent teams struggle for two reasons:
  1. Failures are hard to see clearly. Production conversations are noisy, subjective, and difficult to debug.
  2. Fixes don’t stick. An issue is patched once, but nothing prevents the same failure from returning later.
Conversational agents are harder to evaluate than single-step agents because they don’t fail in isolation - they fail across turns, in ways that only emerge through dialogue. A failure at turn five requires replaying turns one through four every time a fix is made. Multiplied across user personas and scenarios, iteration becomes slow and expensive. In practice, this friction leads teams to ship anyway, hoping the failures won’t matter. They do. The Curation Loop addresses this by:
  • Making failures explicit and reviewable
  • Converting real failures into replayable test cases
  • Continuously growing a living test suite that defines agent quality

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Two Loops, One Goal

The Curation Loop consists of two connected loops:
  • Inner Loop (Development): fast iteration before shipping
  • Outer Loop (Production): learning from real user conversations
Both loops feed into the same outcome: confidence. Curation Loop

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Inner Loop: Curation During Development

The inner loop runs while the agent is actively being built or refined. Models are configured, prompts are crafted, tools are designed, and nothing works perfectly the first time.

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1. Start With Vibe Evaluation

Before metrics and dashboards, talk to the agent. Run a handful of representative conversations and ask:
  • Does the response feel helpful?
  • Is the tone appropriate?
  • Is the agent reasoning correctly?
This qualitative vibe check is how most real issues are discovered early. It is fast, intuitive, and human-aligned. The problem is that it disappears: there is no memory, no regression safety, no way to learn systematically from what went wrong. The inner loop captures it before it is lost.

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2. Spot a Failure

As soon as something feels off (e.g., an incorrect answer, hallucination, poor reasoning, bad tone, or refusal), stop and capture it. Do not rely on memory or screenshots.

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3. Save It as a Test Case

Convert that failure into a structured test case:
  • Input conversation/scenario
  • Expected agent behavior
A failure that isn’t saved is a lost opportunity. Because a wrong step at turn two can compound by turn four, capturing the full conversation, not just the final output, is what allows trajectory failures to be caught early.

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4. Fix the Agent

Make the change: prompt updates, tool usage fixes, guardrails, retrieval changes, or logic updates. The fix itself is not the goal: verifying it is.

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5. Replay Against the Test Suite

Replay the saved test case and the rest of the test suite. If the fix works and does not break existing behavior, the improvement is real. This completes one inner-loop cycle.

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Outer Loop: Curation From Production

Once the agent is live, the most valuable failures come from real users. User research gives hypotheses. Production gives reality.

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1. Observe Production Conversations

Monitor real conversations for unexpected user behavior, edge cases that weren’t anticipated, and subtle failures that never appear in synthetic tests: agents hallucinating on edge cases, looping endlessly on certain inputs, or mishandling scenarios that were never designed for.

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2. Surface a Failure

When a production conversation goes wrong, flag it, review it, and decide if it represents a real gap in behavior. Not every odd conversation becomes a test case; only meaningful failures do. These aren’t failures to be embarrassed by. They are signals. Each one indicates what to build next.

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3. Convert It Into a Test Case

This is the bridge between production and development. A real user failure is transformed into a formal test case and added to the test suite. From this point on, the agent is not allowed to fail this way again.

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4. Fix and Replay

Fix the agent and replay the entire test suite, including old development cases and newly added production cases. This ensures progress without regressions.

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The Test Suite Is the Source of Truth

Over time, the test suite becomes:
  • A record of every important failure observed
  • A definition of what good agent behavior looks like
  • A safety net for future changes
The test suite is not static. It grows as the agent does. Unlike unit tests in deterministic software, conversational agents operate over an infinite input and output space. The only sustainable approach is to treat evaluation as a living artifact, not a one-time checklist.

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Confidence Is the Output

The output of the Curation Loops (inner and outer) is not accuracy scores or dashboards. It is confidence: confidence to ship, to iterate, and to trust that fixes won’t regress silently. The teams that win are not the ones who build the best v1. They are the ones who curate confidence fastest across both loops.
“Treat your agent like an untrusted worker. Curate your confidence, don’t assume it.”

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Key Takeaways

  • Start small and early.
  • Treat every meaningful failure as an asset.
  • Convert production failures into permanent tests.
  • Let the test suite grow with the agent.
  • Confidence is not assumed; it is curated.