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Evaluations and Observability

Aliveo AI continuously measures the quality and safety of every agent so you can trust recommendations in production. This page covers how we evaluate agents before deployment, how we observe them in real time, and how we close the loop with automated and human feedback.

Pre-Deployment Evaluations

  • Scenario test suites: Curated prompts that mirror common marketing workflows (budget pacing, creative analysis, attribution breakdowns) with expected outcomes and acceptable variance. Suites run on every model or orchestration change.
  • Synthetic regressions: Generated edge cases to test tool-call correctness, schema compliance, and recovery paths when APIs return errors, empty results, or slow responses.
  • Scoring signals: Automated graders measure factuality against ground truth datasets, adherence to requested formats (tables, JSON), and guardrail compliance (no speculative claims, no unsupported metrics).

Runtime Observability

  • Structured tracing: Each request is captured as a trace with spans for planning, tool calls, code execution, and grounding steps. Spans include latency, input shapes, and sanitized payload metadata for fast debugging without exposing raw secrets.
  • Metrics: Per-agent dashboards track success rate, latency percentiles, tool-call failure rate, retry counts, and content-filter triggers. Time-windowed rollups highlight regressions after model or connector updates.
  • Artifact capture: Executed SQL or Python snippets, result schemas, and downstream API responses are preserved in secure storage with retention controls, enabling replay and RCA.
  • User-facing explainability: Responses include rationales that outline the agent's reasoning steps, tools used, and corresponding details, helping end users understand and trust outputs. We even store detailed trace links for deeper dives if needed.

Guardrails and Alerts

  • Policy enforcement: Real-time validators block outputs that violate formatting, access controls, or safety rules. Blocked runs emit explicit error codes and remediation hints.
  • Anomaly detection: Baselines watch for spikes in null results, sudden schema drift, or outlier spend metrics. Alerts can notify Slack, or email.

Feedback Loops

  • Inline ratings: End users can rate responses; negative feedback automatically opens tickets with trace links and agent context.
  • Human-in-the-loop review: Queues capture flagged runs (policy violations, high-cost executions, low confidence scores) for manual adjudication before responses are published.
  • Continuous fine-tuning: Approved fixes are folded into evaluation suites and prompt templates, ensuring regressions are caught early. For more details on the feedback loop process, see the Feedback Loops documentation.

By combining rigorous pre-deployment evaluations with robust runtime observability and feedback mechanisms, Aliveo AI ensures that marketing agents perform reliably and safely in production environments.