AI Agent Observability Evaluation

AI Agent Observability Evaluation

Operating contract

Inputs

Outputs

• Produce: trace and outcome scorecard, failure taxonomy, replay evidence, regressions, and release recommendation.

Capability and permission boundaries

Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.

Degraded mode

When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.

Decision rules

Anti-Patterns

• Treating absent evidence as success. Fix: mark the check unassessed and name the missing source.
• Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action.
• Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit.
• Reporting a metric without population, window, or source. Fix: attach all three.
• Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner.

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Design agent evals, task-success metrics, traces, replay, evidence capture, and regression gates.
• Instrument agent runs with events, step logs, artifacts, and customer-visible status.
• Turn agent execution evidence into QA, audit, incident, or product-quality signals.

Do Not Use When

• The work is not AI-specific or agentic-AI-specific.
• A narrower retained AI parent skill fits the request better.

Required Inputs

• Product, tenant, user, data, risk, and operational context relevant to the AI workflow.
• Target artifact: design, implementation plan, audit, test strategy, UX flow, commercial policy, or runbook.
• Constraints from security, privacy, reliability, billing, support, and compliance stakeholders when relevant.

Workflow

1. Read this SKILL.md first.
2. Load references/routing.md to select the absorbed child reference that matches the task.
3. Load only the selected child reference files needed for the current request.
4. Produce execution-oriented output with assumptions, risks, evidence, and next actions where relevant.

Quality Standards

• Keep routing explicit: name which reference files were used when the work depends on absorbed material.
• Preserve tenant isolation, auditability, cost controls, safety gates, and operational evidence when they matter.
• Prefer concrete contracts, checklists, tables, schemas, runbooks, and decision records over broad summaries.

Anti-Patterns

• Loading every absorbed reference by default.
• Treating AI-specific billing, compliance, safety, or UX concerns as generic SaaS work without checking AI failure modes.
• Hiding retired skill names; old slugs must remain discoverable through references/routing.md.

Outputs

• A concrete deliverable matched to the request: architecture, implementation plan, audit, policy, runbook, UX flow, test strategy, or operating model.
• The selected consolidated reference files and any assumptions, risks, evidence requirements, or follow-up actions that affect execution.

References

• references/routing.md maps retired child skill slugs to their consolidated reference folders.

Consolidated Child References

• Load references/routing.md to map retired AI child skill slugs to their reference modules.