Trust plane / Phase 1

Observability & FinOps

What happened — and what did it cost?

Make any task reconstructable end to end and attribute its cost: OpenTelemetry GenAI spans carrying trace, thread and task IDs, the acting identity, the policy decision and the token spend.

Use this harness in my journey →
01 / RESPONSIBILITIES
  • Distributed tracing across model calls, tool executions, retrieval, memory and agent runs
  • Correlation IDs that survive handoffs, queues and human approvals
  • Time-travel debugging: replay of prompts, delegations and memory state at failure
  • Quality and drift monitoring, including silent-failure detection
  • Token metering, budgets, chargeback and cost per successful task
  • Agent-versus-human attribution in every log

Accountable owner

SRE / Observability platform, with FinOps

  • Head of SRE — accountable owner
  • Observability engineer — instrumentation and conventions
  • SRE — SLOs, alerting, on-call for agent estates
  • FinOps analyst — budgets, chargeback, unit economics
  • Data engineer — trace pipelines and retention

Ownership and phase are architectural recommendations, not assessed results.

02 / INTERFACES
03 / EVIDENCE
Instrument from the first pilot request. Retrofitting correlation IDs after the fact is the most common and most expensive omission in this whole composition.

Signals to watch

  • Trace completeness — share of tasks fully reconstructable
  • Share of incidents replayable from telemetry alone
  • Cost per successful task and budget burn rate
  • Agent-versus-human attribution coverage in logs
  • Mean time to detect a silent quality regression
04 / ADAPTATION BOUNDARIES

Telemetry collection and aggregation may be tuned. Preserve outcome provenance, privacy, coverage and resource-limit alerts.

How governed change crosses the architecture ↗

Failure modes

  1. Classic APM is blind to the failures that matter here: infinite loops, hallucinated queries, silent quality decay
  2. Roughly 47% of deployed agents are actively monitored; about 68% of organisations cannot distinguish agent from human activity in their logs
  3. Missing correlation IDs, so an incident cannot be replayed or turned into a test
  4. Token spend discovered at the invoice rather than in the budget
05 / REFERENCES

Existing tool and standards snapshot · approximately mid-2026. Examples, not required dependencies; verify current versions before adoption.

Open source

  • OpenTelemetry with GenAI semantic conventions
  • Langfuse
  • Arize Phoenix
  • OpenLLMetry
  • Grafana, Tempo, Loki, Prometheus
  • Jaeger
  • OpenCost for infrastructure cost

Managed

  • Datadog LLM Observability
  • MLflow Tracing
  • Cloud-native agent observability services

Standards

  • OpenTelemetry GenAI semantic conventions (around v1.41; agent-orchestration and MCP-tool spans still draft — design correlation IDs that survive the spec settling)
  • FOCUS cost schema
  • W3C Trace Context

OpenTelemetry is the data plane only: it records, it does not judge. Pair it with the evaluation harness or you will have perfect traces of a system nobody can say is working.