How Do I Track AI Agent Activity and Decisions?
Instrument end-to-end: decision traces, KPI metrics, audit logs, and cost. Every action should be explainable, reversible, and tied to business outcomes.
Executive Summary
Track agents with the same rigor as revenue systems. Capture traces for every step (inputs, outputs, policies, tools, reason codes), metrics for health and outcomes (success, escalations, cost), logs for errors/events, and cost accounting per decision. Store decision records with IDs that join back to CRM/MAP/CDP so leaders can audit actions and correlate to meetings, pipeline, ROAS/CAC, and NRR.
Observability Layers (What to Capture)
| Layer | Definition | Examples | Why it matters | Owner |
|---|---|---|---|---|
| Traces | Step-by-step spans with context | Skill I/O, tools called, approvals | Explains “why” and enables rollback | Platform |
| Metrics | Quantitative series over time | Success %, escalations, latency | SLOs, alerts, capacity planning | RevOps |
| Logs | Discrete events and errors | Rate-limit, validator failures | Debugging and audits | Engineering |
| Cost | Spend per call/run/outcome | Model tokens, API fees, media | ROI & budget enforcement | Finance/Program |
| Decision records | Normalized “who/what/why” row | Offer chosen + reason code | Join to CRM & scorecards | Data/RevOps |
Decision Record — Minimum Schema
| Field | Type | Description | Join/Source | Privacy |
|---|---|---|---|---|
| decision_id | UUID | Unique decision key | Warehouse | Low |
| agent_id / run_id | String | Agent name and run trace | Observability | Low |
| person_id / account_id | String | Target entities (hashed if needed) | CRM/CDP | PII (mask) |
| action | Enum | e.g., CREATE_LIST, SEND_EMAIL | MAP/CRM | Low |
| reason_code | Enum | Why it chose this (offer/timing) | Trace metadata | Low |
| policy_version | String | Policies/validators applied | Policy store | Low |
| result / status | Enum | SUCCESS / ESCALATED / FAIL | Trace/log | Low |
| cost | Decimal | Model + API + media spend | Cost meter | Low |
| kpi_link | FK | Meeting/opportunity/roas id | CRM/Ads | Low |
Dashboards to Run the Program
Metrics & Benchmarks
| Metric | Formula | Target/Range | Stage | Notes |
|---|---|---|---|---|
| Trace coverage | Runs with full spans ÷ total | ≥ 95% | Any | Critical for RCA |
| Sensitive action success | Successful ÷ total | ≥ 98% canary / ≥ 99% prod | Execute+ | Create list, send, publish |
| Escalation rate | Escalations ÷ sensitive actions | ≤ 2–5% and trending down | Any | Risk signal |
| Cost per outcome | Agent spend ÷ KPI units | −15–30% vs baseline | Optimize | Meetings, pipeline, ROAS |
| Decision explainability | Decisions with reason_code | 100% | Any | Required for audit |
Governance and Access Controls
| Control | Definition | Why it matters | Retention | Notes |
|---|---|---|---|---|
| RBAC & partitions | Role/region isolation of data and logs | Limit blast radius & PII exposure | PII masked; TTL per region | Hash IDs for analytics |
| Policy validators | Automated checks before actions | Blocks unsafe outputs | Versions stored | Log policy_version in trace |
| Kill-switch & rollback | Per agent/channel/region | Rapid containment | Incident logs 12–24 mo | < 60s to disable |
| Audit trails | Immutable decision records | Compliance & RCA | Per policy (e.g., 2–7 yrs) | Write-once store |
| Cost meters | Per-call/run spend tracking | Budget enforcement & ROI | Finance policy | Alert on anomalies |
Deeper Detail
Implement observability at the platform level, not per use case. Standardize a decision-record schema and emit spans for every step—prompt, retrieved facts, tool calls, policy checks, outputs, confidence, and chosen alternative. Include links back to affected CRM/MAP/ads records so reviewers can jump into context and revert safely.
Join observability with business outcomes. Mirror decision records into your warehouse and connect them to meetings, opportunities, spend, and NRR. Ship dashboards for operations (SLOs, error classes), risk (violations, escalations, time-to-kill), finance (cost per outcome), and marketing (lift vs control). Use reason codes to compare agent judgment against human baselines and to train memory and policies.
Finally, govern access and retention. Mask PII, partition by brand/region, and keep audit trails in an immutable store. For architecture and guardrail patterns, see Agentic AI, implement via the AI Agent Guide, build adoption with the AI Revenue Enablement Guide, and validate prerequisites using the AI Assessment.
Additional Resources
Frequently Asked Questions
Wrap your skills/actions with a shared tracing library so every step emits spans and reason codes by default—no custom work per use case.
Mask or hash identifiers, restrict raw payloads, and store pointers (record IDs) instead of values. Partition data by brand/region and set TTLs.
Decision records joined to meetings, pipeline, and spend. Track lift vs control and cost per outcome on a single executive scorecard.
No. Log traces and decision rows in your platform store; mirror to a warehouse later for cross-system reporting and retention control.
Follow regional policy (often 2–7 years). Keep PII minimized, store versions of policies used, and archive immutable copies for audit.