AI Observability & Monitoring
You cannot run in production what you cannot see. Track cost and tokens, latency, quality and a full trace of every agent run, rolled up by team — with alerts the moment something drifts, so AI behaves like any other production system you watch.
Overview
AI that shines in a demo can misbehave at scale — slow, expensive or quietly wrong. Observability is how you catch that before a customer does, by making every run inspectable.
Each agent run is traced: which model answered, what it cost, how long it took and how it scored. Roll those up by team, set thresholds and get alerted on drift — so you operate AI with the same rigour as the rest of your stack.
Explore AI Intelligence Layer →- ✓ Token & cost tracking
- ✓ Latency monitoring
- ✓ Continuous quality scoring
- ✓ Full run traces
- ✓ Per-team usage roll-ups
- ✓ Alerts & thresholds on drift
What you can see
Cost & tokens
Track spend and token use by agent, model and team — no black-box bill at month end.
Latency
See how long each step and each run takes, and where a run stalls.
Quality scores
Score outputs continuously and watch for drift away from the bar.
Run traces
A full trace of every run — the prompts, the tools it called and the decisions it made.
Usage by team
Roll up who is using what, and how much, across departments.
Alerts
Get told the moment cost, latency or quality crosses a threshold you set.
What one run shows you
A trace is the flight recorder for an agent run — so when something looks off, you can see exactly why.
Prompts & context
What the agent was asked and what it was grounded on, step by step.
Tool calls
Which actions and lookups it ran, and what each one returned.
Model routing
Which of the five tiers answered each step, and why that one.
Timing
How long each step took, so you can find the slow hop.
Cost breakdown
Tokens and spend attributed per step, per model.
Outcome & score
The result, its quality score and whether a person stepped in.
Live in four steps
Instrument agents
Every run is traced automatically, with no extra wiring.
Roll up metrics
Cost, latency and quality aggregate by agent and team.
Watch & alert
Thresholds trigger alerts the moment something drifts.
Tune
Fix the slow, costly or low-quality runs you surface.
How observability watches the agent
The Flow-Cognition Agent runs a real loop on your data — grounded in your records, governed and logged.
Sense
Reads every agent run and the metrics it produced.
Decide
Scores cost, latency and quality against your thresholds.
Act
Alerts and surfaces what to fix — and the finding is logged.
Five-tier model routing · field-level permissions · full audit trail. See the AI layer →
Where teams put it to work
Production rollout
Run AI at scale knowing exactly what it costs and how it performs.
Cost control
Catch runaway spend before the bill does, and see which team drove it.
Quality assurance
Watch quality continuously, not just at launch, and alert on drift.
Where it fits
Three jobs, cleanly separated — so each does one thing well.
Observability
Shows what happened in production — the traces, cost, latency and quality of real runs.
Questions, answered
Token and cost, latency, quality scores, full run traces, per-team usage and alerts on drift.
Yes. Every agent run has a full trace of its prompts, tool calls, routing, timing, cost and outcome.
It surfaces and alerts on cost; the hard spend caps live in AI Governance.
Yes, the moment cost, latency or quality crosses a threshold you set.
Evaluations prove quality before release; observability watches it continuously once the agent is live.
Yes. Cost, tokens and runs roll up by agent, model and team so you can see who is using what.
See what your agents are really doing
Start free, or get a guided walkthrough with our team — on the one platform that runs the AI Intelligence Layer and your whole business.