Observability & FinOps

• 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

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

Architectural recommendation, not a research finding.

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

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

Tool and standards snapshot · last reviewed approximately mid-2026