End-to-end tracing
Every run captured as one trace: prompts, model calls, tool invocations, retrievals, and final output, with timing on each step. When something looks wrong, you replay the exact run — not a reconstruction.
Observability for AI agents
Your agents are running. Can you see what they're doing?
AI agents write, decide, spend, and act — often invisibly. Aipraxio builds the observability layer for agent operations: a complete trace of every run, what it cost, whether it worked, and where it's quietly going wrong — so you can run agents with confidence instead of hope.
agent.run12.4s
llm.plan1.7s
tool.crm_lookup0.9s
tool.knowledge_search2.3s
llm.draft_reply3.2s
eval.groundedness 0.940.6s
guardrail.pii_check pass0.4s
Illustrative trace. Every span — model calls, tool calls, evaluations, guardrails — captured in one timeline.
The problem
Traditional monitoring was built for software that behaves. Agents don't.
A normal application either works or throws an error. An agent can return a perfectly successful response that is confidently wrong — a made-up policy, a misread record, a tool called with the wrong arguments. Your dashboards stay green while your customers get bad answers.
Silent failures
Wrong-but-plausible output passes every uptime check. Nothing pages you — until customers complain.
No two runs alike
Agents are non-deterministic: the same request can take a different path each time, so averages hide the runs that matter.
Cost opacity
Token spend arrives as one monthly bill. Which agent, user, or task burned it — and was it worth it — is anyone's guess.
Black-box debugging
When a run goes wrong, the prompts, tool calls, and decisions that caused it are usually gone. You're debugging from memory.
Quiet drift
A model update, a changed document, a new tool — behavior shifts underneath you, and nobody notices for weeks.
Solutions
What agent observability actually includes
Cost & performance monitoring
Tokens, latency, and spend broken down by agent, task, user, and model — with budget alerts before the bill surprises you, and cost tracked per successful outcome, not just per call.
Quality & evaluation signals
Continuous online evaluation layered on live traffic: groundedness against your sources, task success, policy compliance — plus human review queues for the runs scores can't settle.
Anomaly detection & alerts
Alert on what actually hurts: quality drops, tool-error spikes, runaway loops, latency regressions, and cost anomalies. Not just "the service is up," but "the agent got worse."
Behavior analytics
Insight for operators: what users actually ask, where agents succeed and stall, which topics drive escalations. The map of what to improve next, drawn from real runs.
Governance & audit
Immutable records of what each agent saw, decided, and did — with PII redaction and guardrail logs — so compliance, security, and post-incident reviews have something real to work from.
Use cases
Observability, by the agents you run
Different agents fail differently. Here's what operators watch for in each.
Customer support agents
Catch wrong answers before they become tickets: groundedness scores on every reply, escalation-reason analytics, and alerts when resolution quality dips after a change.
Coding & DevOps agents
Cost per merged change, tool-failure hot spots, and traces that show exactly which files, commands, and tests a run touched when a deployment goes sideways.
Sales & outreach agents
Tone and compliance monitoring on outbound messages, conversion tracked per conversation, and instant flags when an agent promises something it shouldn't.
Research & data agents
Source-level provenance for every claim, freshness checks on retrieved data, and drift alerts when the underlying sources or models change behavior.
Multi-agent systems
One trace across agents and handoffs, so you can see where work stalls, loops between agents, or degrades as it passes from one to the next.
Regulated operations
For finance, healthcare, and insurance: complete decision audit trails — inputs, reasoning steps, tool actions, approvals — ready for reviewers and regulators.
The playbook
How to monitor AI agents, in six moves
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1
Trace every run, end to end
Instrument agents with open standards (OpenTelemetry's GenAI conventions) so prompts, model calls, and tool calls land in one timeline — across frameworks and vendors.
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2
Measure outcomes, not just uptime
Task success, resolution rate, escalation rate. An agent that's fast, cheap, and wrong is not healthy — your metrics should say so.
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3
Put cost next to value
Track spend per resolved ticket, per merged change, per qualified lead. Cost alone is a bill; cost per outcome is a decision tool.
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4
Evaluate continuously
Run sampled evaluations on live traffic — groundedness, relevance, policy checks — and route low-confidence runs to human review before patterns become incidents.
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5
Alert on quality and cost, not only errors
Quality drops, loop detection, token-spend anomalies: the failures that hurt agent operations rarely look like crashes, so your alerts shouldn't wait for one.
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6
Close the loop
Recorded traces become regression tests. Every prompt, model, or tool change is checked against real past runs before it ships — insights turn into improvements, measurably.
Our approach
Agents you can operate, not just launch.
Launching an agent is the easy part — the demos always work. Operating one for a year, through model updates, shifting data, and real users, is the actual problem. Aipraxio is focused there: the traces, signals, and insight that make agents operable. It's in the name — praxio, practice: agents in practice, under real conditions, watched properly.
Running agents you can't see into?
Tell us what you're operating and where it's opaque. We'll talk through what to instrument first — and what the traces will show you.
[email protected]