How Creao Turned Agent Exploration Into Repeatable Execution

Creao helps people turn successful conversations into reusable AI agents and workflows. But getting one result right is only the beginning. The harder challenge is making the next run start with what the agent has already learned.

Reflexio gave Creao a learning layer for that transition: profiles preserve useful context, playbooks capture repeatable procedures, and evaluation shows whether retrieved guidance helped or hurt the session.

The impact at a glance

The three outcomes combine Creao’s internal comparison with Reflexio’s retrieved-learning evaluation.

The headline result is a shift from exploration to execution. When the platform recognizes a task it has already learned, it can reuse a saved procedure and configuration instead of rediscovering the path every time.

The challenge: successful sessions did not automatically compound

Agent sessions contain valuable operational knowledge: the format a user expects, the sequence that reliably completes a task, the verification step that prevents a bad output, or the fallback that works when a preferred tool is unavailable.

Without a structured learning layer, that knowledge is easy to lose. The next session may repeat the same exploration, spend more model calls rebuilding context, or omit an action that previously proved necessary.

Creao needed a way to carry forward what mattered without turning every prior conversation into permanent prompt baggage.

The solution: retrieve the right learning at the right moment

Reflexio separates two kinds of knowledge:

• Profiles retain durable context about the user and their environment, such as output conventions or production constraints.
• Playbooks retain procedures: the ordered actions, checks, and fallbacks that make a class of task repeatable.

At runtime, the agent retrieves only the learnings relevant to the current task. Reflexio then evaluates the effect of those learnings so the team can see whether they improved the outcome, had no material effect, or introduced a regression.

This creates a practical loop: observe what worked, structure it as reusable guidance, retrieve it selectively, and measure the result.

Less exploration, fewer model calls

Across Creao’s 28-day internal evaluation, scheduled playbook runs used a median of 2 model calls per delivered outcome, compared with 8 calls for freeform chat tasks. This is an observed comparison across different task cohorts, not a causal estimate.

That is a 75% difference in median model calls. The result is consistent with playbooks reducing repeated exploration, but the comparison does not isolate how much of the difference was caused by the playbook itself.

The same evaluation covered 465,305 runs. Among 463,617 settled playbook runs, Creao reported a 99.0% success rate. Most of those runs—95.2%—executed without a human trigger.

A learned procedure gives the agent a stronger starting point. The value is not more context; it is less rediscovery.

Better outcomes, not just shorter paths

Efficiency only matters if quality holds. Creao used an automated quality judge’s “Poor” verdict as the task-failure measure. Across 230,477 evaluations, Creao reported a 36% reduction in “Poor” verdicts for playbook threads, calculated from unrounded rates; the displayed 5.9% and 3.7% figures are rounded to one decimal place.

Playbook threads received fewer “Poor” automated quality verdicts than freeform threads.

The comparison is directional rather than a randomized causal estimate: playbook tasks are narrower than open-ended chat by design. That narrowing is also part of the product value—turning a path that once required exploration into a repeatable operation.

Retrieved learnings usually helped—or stayed out of the way

Reflexio’s evaluation of Creao’s retrieved learnings tells a complementary story across 2,176 impact verdicts covering 1,228 target interactions.

Retrieved learnings were much more likely to help than hurt, and most were relevant to the task.

Neutral impact is expected. A retrieved learning can be applicable without becoming decisive in every session. The small negative share matters because it shows that selective retrieval was far more likely to help than to introduce harmful guidance.

What this looked like in successful sessions

The aggregate metrics become clearer in the work itself. These are selected lines from two playbooks that were actually retrieved in successful Creao sessions, followed by the recorded evaluation—not a reconstructed example.

Preserve the original image, pixel for pixel

The user wanted an image background removed without changing the subject, its colors, text, edges, or fine details. The retrieved playbook gave the agent a concrete, verifiable procedure:

Retrieved playbook · selected lines

Do NOT call the generate_image tool for background-removal tasks that require pixel-perfect preservation of the subject.

1. Open the source image with PIL and inspect its mode, size, and corner/background pixel values.
2. Apply a tool like rembg or a NumPy color-distance mask to produce an alpha channel.
3. Save as PNG.
4. Verify by sampling the result: a corner pixel should have alpha=0 and a center foreground pixel should have alpha=255.

The evaluator found that the agent followed the playbook precisely: it inspected the pixels, built the color-distance mask, preserved the source, and verified transparency at both background and foreground points. The evaluator described this as the core guidance that drove the successful approach.

Carry a generated image all the way to a delivered video

In another session, the task was not complete when an image existed; the user needed a finished short video. The retrieved playbook preserved the production sequence and the definition of “done”:

Retrieved playbook · selected lines

After generate_image returns a saved image path, pass that exact path as the firstImage parameter to generate_video.

Poll the returned task ID with query_video_generation_status repeatedly—generation typically takes 1–5 minutes, so an initial “still pending” response is normal and must not be treated as a failure.

The video is ready when the status returns a FILE_DOWNLOAD entry; use the local file path inside that entry as the final deliverable to the user.

Avoid re-running generate_image…reuse the already-saved image so the video stays visually consistent with what the user just saw.

The evaluator confirmed that the agent reused the saved image, initiated video generation, kept polling through the pending state, and delivered the completed file. The playbook helped turn an intermediate asset into the outcome the user had actually requested.

These are observational examples, not controlled causal measurements on their own. The retrieved-learning verdicts and Creao’s aggregate comparison provide the broader evidence.

The result: an agent that compounds from experience

For Creao, Reflexio made previous work operational. Useful context could persist, successful procedures could repeat, and the effect of retrieved guidance could be measured instead of assumed.

The result was a system that used fewer model calls, produced fewer poor outcomes, and received positive impact from retrieved learnings far more often than negative impact.

That is the practical difference between an agent that remembers and an agent that improves: the next session starts closer to the outcome.

Measurement notes

Creao’s internal comparison covered 28 days and 465,305 runs; the quality comparison covered 230,477 automated evaluations. The model-call figures compare different task cohorts and are observational rather than causal. The reported 36% reduction uses Creao’s unrounded “Poor” verdict rates, while 5.9% and 3.7% are rounded display values. Reflexio’s retrieved-learning analysis covered a separate 30-day export from July 4 through August 3, 2026. System-level failures such as insufficient credit or no response were not treated as evidence that a profile or playbook helped or hurt task execution.