# Fix the pattern. Improve the agent. Product: Evals & learning Turn recurring conversation issues into targeted instruction and flow improvements. The current suggested-fix pilot keeps every change behind validation and reviewer approval. Interactive product examples use illustrative data; changes remain on the page. ## From an observed issue to a reviewed improvement. An agent repeatedly leaves after-hours requests unclear. Use those findings to propose a focused change and examine its behavior before applying it. ### Propose the change Group related findings and generate a targeted instruction or supported flow-field edit. Keep the proposed change connected to the issue it addresses. ### Review the validation Replay candidate responses against the rule and inspect regression cases from earlier fixes. See the trials behind the result. ### Approve the improvement A reviewer checks the edit and validation evidence before it is applied. Every suggested fix requires manual approval in the current pilot. ## Build improvement into the operating process. ### Make focused changes Address a recurring weakness in the relevant instructions or flow fields instead of rewriting the whole agent. ### Keep earlier lessons in view Use retained regression cases to assess whether a new change preserves the behavior your team already improved. ### Give reviewers the context Bring the source finding, proposed edit and validation results together for a more informed decision. ## Review the change and its testing boundary. Use validation to inform a decision, with additional checks for the actions and channels outside text replay. ### Confirm pilot access Suggested fixes are a pilot capability. Our team can confirm the settings and review process available to your account; automatic application is disabled. ### Keep external actions separate Replay does not execute live tools or webhooks. Voice behavior and external results need their own testing before relying on the change. ### Improve configuration Learning here means evaluated instruction and supported flow changes. It does not retrain a foundation model or guarantee future accuracy. ## Product capabilities and architecture Assistable Evals & learning connects observation findings with proposed changes to agent instructions and supported flow fields. Candidates undergo synthetic replay and regression checks. A reviewer approves each fix before application in the current pilot. ### From a quality finding to a tested proposal. ### Grouped issues and focused proposals Bring related findings together and generate a targeted improvement. Account settings control scheduled suggestion generation. ### Candidate replay Evaluate regenerated candidate responses against the observation rule. The review includes the trials behind the result, rather than the drafting model’s own rating. ### Regression checks Use retained cases from successful fixes to assess later changes. Keep earlier lessons available as the agent’s instructions evolve. ### Prompt and supported flow edits Suggest instruction changes and prompt-routing conditions on existing flow elements. Broader graph design, integrations and tool configuration remain in the builder. ### Evidence attached to the change. Keep the observed issue, candidate behavior and source configuration attached to the change. ### Separate proposal from validation The system drafts a candidate and evaluates its behavior. Confidence comes from conclusive replay trials. ### Replay without live side effects Synthetic text replay excludes live bookings, webhooks and external tool effects. Tool-dependent cases require manual review and testing. ### Apply against the right version Source checks catch newer edits or conflicting drafts before applying a fix. ## Operating controls and references ### Review every change before it is applied. Suggested fixes are currently in a pilot with manual approval. Confirm availability and the review process for your account. ### Inspect the proposed improvement Review the edit, its source finding and validation results. See how the candidate differs before changing the agent. ### Keep a reviewer in control Every fix requires approval after validation in the current pilot. Automatic application is disabled, including for call-derived and shared-flow changes. ### Understand the replay boundary Replay evaluates text behavior without executing live tools or webhooks. Voice behavior, blocked actions and external outcomes need separate testing. - [Agent design and versioning](https://www.assistable.ai/platform/flow-builder) - [Operating controls for AI agents](https://www.assistable.ai/product) - [Define the deployment scope](https://www.assistable.ai/enterprise-voice-ai) ## Connect the next part of your workflow. - [Observations](https://www.assistable.ai/solutions/observations): Know which conversations need attention. - [Flow Builder](https://www.assistable.ai/solutions/flow-builder): Build the questions, branches and actions your process needs. - [Call tracing](https://www.assistable.ai/solutions/call-tracing): Understand the call. Resolve the issue. ## Evals & learning questions. ### What does self-learning mean in Assistable? It means using observed issues to propose and evaluate configuration improvements, including instructions and supported flow fields. It does not retrain or fine-tune the underlying foundation model. ### Does Assistable apply suggested fixes automatically? No. In the current pilot, a reviewer must approve every fix after validation. Automatic application is disabled. Our team can confirm feature access and the review process available to your account. ### Does replay execute real bookings or calls? No. Replay is synthetic text interaction without live external tool effects. Cases that depend on those actions require additional testing and review. ### What does the confidence score represent? It reflects resolved trials among conclusive replay trials under the validation policy. It is evidence about those tests, not a guarantee of future accuracy. Feature access and application settings are confirmed for the account. ## Make your next improvement a considered one. Show us the recurring behavior you want to improve. We’ll walk through the available proposal, validation and approval process. [Talk to sales](https://www.assistable.ai/contact) Source: https://www.assistable.ai/solutions/evals