See What Your Agents Actually Did
Full-trajectory traces of every model call, tool call, retrieval, and decision. Eval-wired monitoring, alert thresholds that mean something, and cost attributed to the call that caused it.
Your APM shows a 200 in 4.2 seconds and hides the dozen model calls underneath where the run went wrong. We instrument agents so every run is a readable trajectory, on your stack or ours, whether we built the agents or someone else did. This is the same observability layer we run on our own products and our banking agent at ~500K actions a month.
- AGENT run: reconcile_invoice
- LLM plan · claude-sonnet
- RETRIEVER policy_docs · stale index
- TOOL erp.lookup_vendor({id: 4471})
- LLM draft · cited 2 docs
- GUARDRAIL policy check
- EVALUATOR faithfulness score
How Teams Fly Blind on Agents
Agent failures are silent by default: the system completes the request and still retrieves the wrong document, loops on a bad tool call, or burns budget on a retry storm. These six blind spots recur across agent systems we get called in to instrument.
No Failing Step in the Logs
Logs capture the final input and output and throw away the middle, which is where agents fail. A report of "it gave a weird answer" tells you nothing about which of the fourteen steps produced it.
APM Green, Agent Wrong
The dashboard shows 200 OK in 4.2 seconds while the agent retrieved the wrong document, called a tool with truncated JSON, retried twice, and answered confidently anyway. Request health hides agent failure.
Sampled Away the Evidence
Agent telemetry is heavy, so teams sample or drop it to control cost, and lose the failed runs they needed for the postmortem. Sampling drops whole trajectories, not single calls.
Cost Discovered on the Invoice
Token spend is a runtime variable: a retry loop or verbose chain-of-thought can triple the bill overnight. With no per-call attribution, nobody can name the feature, tenant, or step that caused it.
Silent Behavioral Drift
A model update, prompt tweak, or data shift erodes answer quality one release at a time. With no eval trend, the drift stays invisible until customers notice, and nobody can name the release that caused it.
Dashboards Nobody Acts On
A wall of charts with no thresholds, no owners, and no escalation path is decoration. When a metric moves, nothing happens, and the next incident review starts from zero again.
Trace It, Watch It, Grade It
Observability for agents is three disciplines, not one dashboard. Each answers a different question, and a system missing any of them flies blind in a different way.
Tracing: What Happened?
Every run captured as a tree of spans: each model call with its context, each tool call with arguments and result, each retrieval and guardrail check. The trajectory, not the API call.
Monitoring: Is It Healthy?
The aggregate layer across thousands of runs: latency and cost trends, error and retry rates, escalation frequency, drift signals. Dashboards with thresholds and owners, so a moving metric produces an alert.
Evaluation: Was It Good?
Traces show what the agent did; evals say whether it did it well. Golden-set scores tracked per release turn quality into a trend line with regression alerts, so a bad release gets caught before customers feel it.
What Gets Captured on Every Run
Following OpenTelemetry GenAI conventions, so the data is portable and not locked to one vendor's dashboard. A trace is only as useful as the data on its spans, so we capture payloads, not just timings.
The span inventory
- Agent span: the outer run with identity, session, and tenant context every child inherits
- LLM spans: full message history, model name, token counts, latency, and cost per call
- Tool spans: tool name, the exact arguments the model produced, and the result it got back
- Retriever and reranker spans: what was searched, what came back, what survived the rerank
- Guardrail spans: policy checks, PII screens, and injection defenses, pass or flag
- Evaluator spans: faithfulness and quality scores attached to the run that produced them
- Decision records: why the agent chose a path, checkpoint outcomes, human overrides
- Business context: user tier, feature, and workflow, so cost and quality map to outcomes
How an Observability Engagement Lands
Five steps from "we cannot see anything" to a system your team runs. Works on agents we built or agents someone else did.
Telemetry Audit
WEEKS 1-2 · INSIDE THE STUDYWe map what your agents emit today, where the blind spots sit, and which questions your team cannot answer. The output is an instrumentation plan and a costed estimate.
Instrumentation
OPEN STANDARDS, NOT LOCK-INAgents get instrumented with OTel-compliant spans: model calls, tool calls, retrievals, guardrails, evaluators. Works alongside LangChain, CrewAI, or custom orchestration, into your existing stack or one we stand up.
Eval Wiring
QUALITY AS A TREND LINEThe eval harness connects to the trace stream, so scores attach to real runs and quality becomes a tracked metric with regression alerts per release.
Dashboards & Thresholds
METRICS WITH OWNERSDashboards for the signals that matter: latency, cost, error and retry rates, escalation frequency, eval trend. Every metric gets a threshold and an owner, so movement produces action.
Handover & Runbook
YOURS TO RUNYou get the trace schema, dashboards, alert thresholds, cost attribution model, and an incident runbook for reading trajectories when a run goes wrong, in your accounts, with full IP assignment.
Why Our Observability Ships Faster
Most engagements design the trace schema, eval pipeline, and cost model from scratch. Ours start from the observability layer that already runs our products and our banking agent in production.
What every deployment carries
- Full-trajectory traces: every span captured, payloads includedEVERY RUN
- Tool-call records: arguments and results, not just timingsEVERY CALL
- Decision records: why the agent chose, who approved, what overrodeEVERY DECISION
- Eval trend: golden-set scores tracked per release with regression alertsEVERY RELEASE
- Feedback loop: human overrides and failures feed the eval setCONTINUOUS
- Governance hooks: traces feed the enforcement layer tooALWAYS ON
- Operations: incident runbooks for reading trajectories under pressureALWAYS ON
- Cost bounds: per-call, per-feature, per-tenant attribution engineered inCONTINUOUS
What You Own at Handover
An instrumented agent system and everything needed to read, alert on, and act from its telemetry, in your accounts from day one.
The observability handover
- Agents instrumented with OTel-compliant spans across every run
- Trace schema: the span inventory, attributes, and retention policy documented
- Eval harness wired to the trace stream, scoring real runs per release
- Dashboards for latency, cost, errors, retries, escalations, and eval trend
- Alert thresholds with named owners and escalation paths
- Cost attribution model: per call, per feature, per tenant
- Feedback loop: overrides and failures feeding the eval set
- Incident runbook: how to read a trajectory and find the failing span
- Post-launch observation window on live telemetry
Scoped by a Feasibility Study, Fixed at Kickoff*
The study audits your current telemetry and prices the instrumentation build. The engagement wires traces, evals, dashboards, and alert thresholds on live agents. After handover, you choose what continues.
Feasibility Study
Audits your current telemetry, maps the blind spots against the questions your team cannot answer, and prices the instrumentation build. Ends with a go or no-go verdict and a costed estimate.
The Instrumentation Build
Scope, timeline, and fee locked at kickoff. Weekly demos show live trajectories, eval trends, and dashboards coming online against your real agents, so progress is visible in your own telemetry.
Operate or Hand Over
An observation window on live telemetry is included. Then a retainer to run the observability function, or a clean handover with the schema, dashboards, and runbook in your accounts.
*Build pricing depends on scope and is confirmed in the feasibility study. The number is locked before work starts.
APM Dashboards, DIY Tooling, and Operator Engineers
Observability splits between request-level monitoring, tools that sell you a dashboard, and teams that run instrumented agents in production. The difference shows up the first time a run fails.
Request health: status, latency, error rate.
Whatever the default instrumentation emits.
The full trajectory: every model call, tool call, retrieval, and decision.
It shows 200 OK anyway.
Depends on who set up the sampling.
You open the trace and see the flagged span.
Not measured.
A dashboard someone meant to wire up.
Eval scores attached to real runs, trended per release.
Host-level metrics.
Token totals, maybe.
Per call, per feature, per tenant, engineered as a bound.
Vendor benchmarks.
A license purchase.
~500K traced agent actions/month in PCI-DSS production.
| Dimension | APM Vendors | DIY Tooling | FLYTEBIT |
|---|---|---|---|
| What you see | Request health: status, latency, error rate. | Whatever the default instrumentation emits. | The full trajectory: every model call, tool call, retrieval, and decision. |
| When a run fails | It shows 200 OK anyway. | Depends on who set up the sampling. | You open the trace and see the flagged span. |
| Quality | Not measured. | A dashboard someone meant to wire up. | Eval scores attached to real runs, trended per release. |
| Cost | Host-level metrics. | Token totals, maybe. | Per call, per feature, per tenant, engineered as a bound. |
| Proof | Vendor benchmarks. | A license purchase. | ~500K traced agent actions/month in PCI-DSS production. |
Match the Tool to the Question
Observability answers what the agents did. If that is not your question, one of these fits better.
Frequently Asked Questions
What does agentic AI observability include?
Instrumenting your agents so every run is captured as a structured trajectory: each model call with its full context, each tool call with its arguments and result, each retrieval, guardrail check, and decision, plus the cost of every step. On top of the traces sit monitoring dashboards, an eval harness tracking quality over time, alert thresholds, and per-call cost attribution.
How is this different from the APM we already have?
Traditional APM sees request-level health: status 200 in 4.2 seconds. It hides the 12 model calls, 4 tool invocations, and 2 agent handoffs underneath, any one of which can be where the run went wrong. Agent observability captures the whole run as a trace of spans, so "the agent failed" becomes "the retriever returned a stale document on step three."
Can you add observability to agents we already run?
Yes, retrofit is the common case. We instrument existing agent systems regardless of who built them: frameworks like LangChain, CrewAI, or custom orchestration, on your current observability stack or one we stand up. Instrumentation follows OpenTelemetry GenAI conventions, so the data is not locked to one vendor's dashboard.
Which observability tools do you use?
We are tool-agnostic. If you already run Datadog, LangSmith, Langfuse, Arize, or an open-source OTel stack, we instrument into it. If you have nothing, we stand up a proven stack in your accounts. The trace schema follows open conventions, so switching tools later does not mean re-instrumenting.
How do you keep telemetry costs under control?
With cost bounds, not cost alerts. Agents produce far more telemetry than conventional requests, so naive full-fidelity capture gets expensive fast. We structure what gets stored, retain full fidelity where it matters, including failed and flagged runs, and attribute storage and inference cost per feature and per tenant, so the telemetry bill is engineered rather than discovered.
How long does an observability engagement take?
Instrumenting an existing agent system typically takes 3 to 6 weeks depending on the number of agents and the stack. The trace schema, eval harness, and dashboards already exist from our own products, so builds start from proven components. The exact window is set in the feasibility study.
If we have observability, can we monitor and control agent issues?
Monitoring, yes: observability answers what the agent did, how long it took, which tools it called, and what it cost. Control is a different layer. Governance answers whether the agent should have done it, whether the action was evaluated against policy before it ran, and whether the output met acceptance criteria. Observability provides the data; governance turns it into a judgment call. Most teams need both, and the audit trail has to come from a system the agent does not control.
How much do agentic AI observability services cost?
Cost depends on scope: number of agents, spans to instrument, and whether we build on your existing stack. The feasibility study, which starts from $2K, produces a costed estimate. The build price is then fixed at kickoff, so the number is agreed before work starts.
Open the Trajectory, Find the Span
Schedule a 30-minute working session with our expert team. We will look at what your agents emit today and give you a straight answer on which blind spots matter first.