Feedback loops: a cornerstone of continuous improvement for AI agents
The feedback loop is an essential pillar for the continuous improvement of AI agents. It transforms every failure or edge case encountered in the real world into a learning opportunity and a concrete test. For AI models in production, this loop is crucial because performance can degrade due to changes in data or environment, making constant iteration and monitoring indispensable.
The essence of the feedback loop
A robust feedback loop enables an AI agent to continuously learn, adapt, and become more resilient over time. It is a strategic practice that maintains trust, performance, and alignment with business objectives.
Turning failures into learning opportunities
- Treat real interactions as test scenarios: It is vital to use actual user queries and edge cases to build the agent’s test suite.
- Every failure is a learning opportunity: When an agent fails or underperforms in production, recording the full context (input, output, evaluation) is critical. Detailed logs of interactions—including inputs, outputs, errors, and timestamps—are invaluable for diagnosis and auditing.
Systematic capture and structuring of failures
Effective improvement requires failures to be captured and organized systematically:
- Automated evaluation: Implement automatic evaluators that flag failed outputs based on relevant rules or metrics for the use case (e.g., accuracy, format, latency, compliance). Platforms now allow non-technical teams to “test, improve, and deploy AI agents with confidence.”
- Transform failed outputs into new test scenarios: When an output is marked as failed (by an evaluator or human review), it should be immediately added as a new test case.
- Enrich the scenario base: The more the agent is used in production, the more robust and representative the test suite becomes.
Shortening the loop for rapid iteration
Prompt engineering is an iterative process of testing, comparing, and refining prompts continuously. Speed is key:
- Rapidly iterate on failed scenarios: Improvement efforts should focus precisely on cases where the agent failed rather than broad, unfocused changes.
- Replay and validate: Improved prompts or logic must be tested against all test scenarios—especially new ones—to ensure past failures are resolved without regressions. Repeat this process until all tests pass using a simple pass/fail logic.
Closing the loop in production
Once deployed, monitoring takes over to maintain vigilance, detecting performance or behavior drifts and raising alarms:
- Continuous live usage monitoring: Constantly record interactions, inputs, outputs, and evaluation results in production. Monitoring is the ongoing tracking of a machine learning model’s behavior after deployment.
- Facilitate failure review and annotation: Establish tools or processes to quickly diagnose failure causes and define expected outputs.
- Promote a “fail fast, fix fast” mentality: Encourage teams to rapidly turn production failures into reproducible tests, then fix and validate them. Feedback loops are central to this process, where every production failure becomes a new test case for prompt improvement. Continuous monitoring—including data drift and performance detection—is essential to keep models aligned with objectives even after deployment.
Best practices for an effective feedback loop
- Automate as much as possible: Use auto-evaluators to flag issues early and at scale.
- Keep the process lightweight: Reduce friction so team members can easily add failed cases as new tests.
- Make improvements visible: Track progress over time as the number of failing scenarios decreases.
- Learn from each iteration: Use new failures as insights to strengthen prompt design, agent logic, or evaluation criteria.
- The virtuous cycle: Insights from production monitoring refine offline evaluation criteria, improving initial evaluation and progressively boosting model robustness.
Concrete example: Poppins
Before implementing a systematic feedback loop, Poppins lacked visibility into prompt performance, hindering rapid iteration and quality maintenance of its AI features. Using Basalt, Poppins was able to:
- Directly manage prompting and analysis, doubling iteration speed from draft to production.
- Conduct over 5,000 evaluations to systematically improve prompts.
- Deploy 15 AI prompts in production in less than a month.
- Create more than 180 test cases in their workspace.
- Iterate through 149 prompt versions to reach high quality.
This example illustrates the tangible impact of a well-structured, operationalized feedback loop on the efficiency and quality of AI agent development.
