TL;DR
AI agent observability means you trace every step of an agent run, measure verified task success, and alert when outcomes drift from a baseline. Trace the plan, each tool call with its arguments and result, every handoff between agents, and the state change in the system of record. Then measure verified task success rate, tool-call accuracy, retries per task, cost per successful task, p95 step latency and human-override rate. Page people for business-impacting signals such as a drop in verified success or a policy violation, and send latency noise to a ticket queue. As of October 2026, OpenTelemetry defines agent span types such as invoke_agent and execute_tool, though those conventions are still in Development status. Traces also double as audit evidence, since the EU AI Act asks deployers of high-risk systems to keep logs for at least six months.
Key Takeaways
AI agent observability records each step of an agent run, so you can rebuild what happened and check the result against the system of record.
A 200 OK from a tool proves the call worked, but it does not prove the business task got done.
Model each run as a session, a trace and spans, using the OpenTelemetry agent span types when your framework supports them.
Measure verified task success, tool-call accuracy, retries, cost per successful task, p95 step latency and human-override rate.
Set every alert threshold from a two to four week baseline, and page people only for business-impacting signals.
Traces double as audit evidence, so redact sensitive data at capture time and keep logs as long as your obligations require.
The Agent Said “Done.” The ERP Disagreed.
Picture an accounts payable agent working through a Monday queue, a typical job for AI agents in finance . It reads a supplier email, finds the invoice and then calls the approval API. When the API returns 200 OK, the agent posts a cheerful note to the finance channel: invoice approved.
On Tuesday morning, the supplier calls to ask why payment is late. Finance opens the ERP and finds the invoice still pending. The agent had pulled a stale invoice ID from session memory, so it approved an invoice that was already approved.
Nothing in the stack looked wrong, because the API worked, latency was normal and no error fired. The scenario is illustrative, but its lesson is general: a successful tool call is not a verified business outcome.
That gap is why agent work needs its own kind of visibility. Agents have moved from single prompts to multi-step workflows, which is a big part of how AI agents have evolved . Each extra step is also one more place for a quiet failure to start. So this guide covers what to trace, what to measure and what to alert on.
What Is AI Agent Observability?
AI agent observability is the practice of recording every step an agent takes, from plan to tool call to final state change. With that record, you can rebuild any run and then check whether the work actually landed.
The easiest way to see the difference is to follow one unit of work. An APM tool sees an HTTP request. By contrast, an LLM observability tool sees one model call with its prompt, response and token count. Data observability , a related discipline, watches the pipelines and tables that feed the agent.
Agent observability sees the whole task instead. That includes the plan, five tool calls, a handoff to another agent and the ERP record that changed or did not.
In a March 30, 2026 release, Gartner predicted LLM observability investments will reach 50% of GenAI deployments by 2028, up from 15% at the time. That matters for agents, because they add tool calls and business outcomes on top of the model layer it covers. The table below shows where each layer of observability starts and stops.
Dimension APM LLM observability AI agent observability
Unit of work A single request or transaction One model call A full task across many steps What fails Errors, timeouts, slow services Ungrounded or low-quality responses Wrong tool, wrong arguments, unverified outcomes Main signal Latency and error rate Prompt, response, tokens, quality score Verified task success, together with the step that broke Who reads it SRE and platform teams ML and application engineers Agent owners, engineers and the business process owner
If your application calls a model once per request, LLM observability may be all you need. Once the model starts choosing tools and acting on business systems, though, you need the agent view on top.
Monitoring, Observability and Evaluation: Three Different Jobs
These three words often get used as if they meant the same thing. Mixing them up leaves gaps that nobody owns, so it helps to separate them early.
Monitoring watches for problems you already know about, such as an error rate above 2%. Observability, on the other hand, lets you rebuild any run after the fact, even failures nobody predicted. Evaluation then scores whether the output was correct, using test sets, rubrics or checks against real data.
Job Question it answers Example in the invoice agent
Monitoring Is a known metric out of range? Alert when approval API errors pass 2% in an hour Observability What exactly happened in this run? The trace shows the invoice ID came from session memory Evaluation Was the result correct? A nightly job compares approvals with ERP status
A production agent needs all three, because each one catches failures the others miss. For the evaluation side in more depth, see our guide to evaluating AI agents .
Why AI Agents Slip Past Traditional Monitoring
Traditional monitoring was built for software that does the same thing every time. Agents choose their own path, so the same input can produce different steps on different days. As a result, the six failures below all look healthy on a standard dashboard.
Silent success. The agent reports the task done while every call returns 200 OK. Still, the target system never changed, or the wrong record changed. Only a check against the system of record catches it.Wrong tool or wrong arguments. An agent may pick a search tool when it needed a lookup, or it may pass a stale ID. Since the call succeeds, no error fires, so the trace has to show the arguments and their source.Retry loops. An agent that misreads a tool response may call the same tool again and again. Each call is fast and cheap on its own. Together, though, they burn tokens and time while the task goes nowhere.Stale or missing context. In agentic RAG pipelines, retrieval may return an old policy, or memory may carry data from a previous session. The answer still sounds confident, because the model cannot tell that its context was wrong.Cost drift with flat request volume. Request counts stay level while spend climbs. For example, a prompt change, a longer plan or a new tool can double the steps per task without anyone noticing.Permission overreach. The agent calls a write action it was never meant to use, or it touches data outside its scope. Access logs may record it, but nobody connects the entry to the task.
What the Six Failures Have in Common
The pattern across all six is the same. A system metric says yes while the business outcome says no.
Closing that gap takes traces that capture intent, arguments and outcomes rather than status codes alone. Each of the six failures maps to one of those three.
Wrong tools and permission overreach are intent failures. Stale IDs and old context are argument failures, while silent success, retry loops and cost drift only show up in outcomes. The rest of this guide shows how to build traces that catch all three.
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Why AI Agents Fail in Production
A short Kanerika take on why agents that pass a demo break once they meet real systems, the same silent failures this section describes.
Anatomy of an Agent Trace
An agent trace is a structured record of one task, broken into timed steps you can read in order. Think of it as a flight recorder for a single job.
The building blocks nest inside each other. A session groups everything one user or process did over time. Inside that session, a trace covers one task, such as approving invoice INV-20937. Spans are then the steps inside the trace: the plan, each model call, each tool call and any handoff to another agent.
Every span carries a start time, an end time, a status and a set of attributes. Attributes hold the details that matter for debugging, such as the tool name, its arguments and the result. A shared task ID on every span then lets you pull the whole run back together, even when it crosses several services.
The Span Types OpenTelemetry Defines for Agents
OpenTelemetry is the open tracing standard that most observability tools accept. Its GenAI agent span conventions name the operations an agent performs, so that traces from different frameworks can share one shape.
Span type What it records Example span name
create_agent An agent being created, together with its name, ID and description create_agent invoice-agent invoke_agent A call to an agent, either as a client call or as an internal step invoke_agent invoice-agent invoke_workflow One run of a defined multi-step workflow invoke_workflow invoice-approval plan The agent working out its next steps before it acts plan invoice-agent execute_tool One tool call with its arguments and result execute_tool approve_invoice Agent as a tool A tool call that transfers work to another agent, so no extra span is added execute_tool for the transfer
Two cautions apply. As of October 2026, these conventions are still in Development status, so names and attributes can change between releases. They also moved to their own repository in 2026, and the GenAI pages of the main semantic conventions (version 1.44.0) point there.
A March 6, 2025 post on AI agent observability from the OpenTelemetry project describes two ways to get these spans. A framework can build the instrumentation in, or else you can add OpenTelemetry instrumentation yourself. Many AI agent frameworks already emit some of these spans, so check what yours sends before you write custom code.
Example: One Tool Span From the Invoice Agent
Here is what one tool span might look like for the invoice agent. The two app.* attributes are your own additions for the business check, so their names are only illustrative.
span: execute_tool approve_invoice
gen_ai.operation.name = "execute_tool"
gen_ai.tool.name = "approve_invoice"
gen_ai.tool.call.id = "call_7f3a"
gen_ai.agent.name = "invoice-agent"
gen_ai.conversation.id = "conv_2291"
app.task.id = "task_4471" # custom: links every span in the task
app.invoice_id.source = "session_memory" # custom: where the argument came from
status = OKThat last custom attribute is the one that would have caught the opening failure. It shows the invoice ID came from memory rather than from the supplier’s email.
What to Capture at Each Step
A useful trace captures enough to rebuild the run and check the outcome, and no more. If you capture too little, you cannot debug. If you capture too much, you store customer data you never needed.
Field Why it matters Redact?
Task ID and session ID Links every span from one job, even across agents and services No User or caller ID Shows who asked, so access reviews and audits have a name Pseudonymize Prompt and model version Explains behavior changes once a new release ships No Retrieved context IDs Shows which documents shaped the answer No, but store IDs rather than full text Tool name, arguments and result Shows what the agent actually did Mask personal and financial fields State change in the target system Proves whether the task landed Mask record contents Tokens and cost Ties spend to tasks rather than to single calls No Latency per step Finds the slow tool or model call No Guardrail decisions Shows what was blocked or allowed, and why Mask the blocked content Human override Records who stepped in and what they changed Pseudonymize
OpenTelemetry’s GenAI span conventions mark full message content, such as gen_ai.output.messages, as opt-in, because it is likely to hold personal data. That default makes sense for most teams. So capture IDs and metadata for every run, and switch on full content only when you have a clear need and a redaction step.
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Traces show what an agent did, but they do not show what happened inside the model. When an agent writes out its reasoning, that text is another model output, so treat it as a claim rather than as evidence. Traces also cannot explain AI hallucinations on their own, though they do show which context the model had when it made one up.
The evidence lives in the systems the agent touched. If the trace says an invoice was approved, then the ERP record has to agree. So build that check into the trace itself, as a span that reads the target system after each write.
Here is a minimal sketch of that read-back span, written with the OpenTelemetry Python API . The erp client and the app.* attribute names are placeholders for your own.
from opentelemetry import trace
from opentelemetry.trace import Status, StatusCode
tracer = trace.get_tracer("invoice-agent")
def verify_approval(erp, task_id, invoice_id):
with tracer.start_as_current_span("erp_readback") as span:
span.set_attribute("app.task.id", task_id)
span.set_attribute("app.invoice.id", invoice_id)
status = erp.get_invoice_status(invoice_id)
span.set_attribute("app.erp.status", status)
if status != "approved":
span.set_status(Status(StatusCode.ERROR, "ERP read-back failed"))
return False
return TrueThe span is marked as an error when the ERP disagrees, so the run counts as failed even though the tool said yes. That one habit catches more silent failures than any volume of reasoning logs.
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Start Your AI Assessment → AI Agent Observability Metrics That Matter
Agent metrics should describe whether work got done, at what cost and with how much human help. Request counts and uptime still matter, but they sit underneath.
A team can celebrate a 99% tool success rate while finance quietly fixes one invoice in ten. The metric was true, yet it measured the wrong thing. That is why the table starts with outcomes.
Metric Formula Alert when (example)
Verified task success rate Tasks confirmed in the system of record ÷ eligible tasks attempted Falls 3 or more points below baseline for an hour Tool-call accuracy Correct tool with valid arguments ÷ graded tool calls Daily sample falls below baseline Retries per task Repeat calls to the same tool ÷ tasks Doubles compared to baseline Cost per successful task Model and tool spend ÷ verified successful tasks Rises 25% week over week while volume stays flat p95 step latency 95th percentile duration for each span type Breaches the user-facing service level Escalation or human-override rate Tasks a person finished or reversed ÷ tasks Jumps compared to baseline Policy-violation rate Guardrail blocks and flagged actions ÷ tasks Any confirmed violation on a write action Drift in evaluation scores Change in average score on a fixed test set Falls after a model, prompt or tool change
The thresholds in the last column are starting points to tune, not standards. For metrics at the level of a single model call, such as groundedness and token use, see our guide to LLM observability .
Setting Alert Thresholds From a Baseline
An alert threshold without a baseline is a guess. Agents vary by design, so a run that takes eight steps one day and eleven the next may be perfectly normal. That is why you need to know what normal looks like before you alert.
Run the agent in production for two to four weeks while tracing is on and paging is off.
Then record the normal range for each metric, by task type and by time of day.
Alert on deviation from that range, rather than on fixed numbers copied from another team.
Page a person only for business-impacting signals, such as verified success or policy violations, and send latency and retry noise to a ticket queue.
Revisit the baseline after every model, prompt or tool change. Otherwise a stale baseline makes a healthy agent look broken, or a broken one look fine.
Observing Multi-Agent Systems
In a multi-agent system, one task passes through several agents, so blame gets lost at every handoff. For instance, a planner hands work to a researcher, which passes notes to a writer, which calls a reviewer. The same chain shows up in AI agents for project management , where one request touches planning, scheduling and reporting agents in turn. When the final answer is wrong, each agent’s own log can still look fine.
The fix starts with one shared task ID that travels with the work. Every agent stamps it on every span. The handoff then becomes a span of its own, so you can see what was passed, in what shape and what came back.
Across service boundaries, the W3C Trace Context standard carries the trace ID in a traceparent header. A downstream agent that reads it joins the parent trace rather than starting a new one.
OpenTelemetry’s agent-as-a-tool pattern also helps here. When one agent transfers work to another through a tool call, the convention records that tool call and adds no extra span. That keeps the trace tree honest about who called whom.
In practice, blame tends to get lost in four places:
Handoffs with no record of the payload passed between agents.
Agents that start a new trace instead of joining the parent trace.
Shared memory that one agent writes while another reads it, with no span for either.
Parallel branches that finish at different times with no join step.
How you split work between agents is a design choice, covered in our guides to AI agent orchestration and AI agent architecture . Observability does not change those patterns, but it does make each one debuggable.
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Tracing a Multi-Agent Workflow?
Talk to Kanerika about shared task IDs, handoff spans and outcome checks for agents that pass work to each other.
Talk to an Expert → Security and Guardrail Signals in Agent Traces
Traces are one of the most useful security sensors you have for agents, because attacks on agents show up as unusual actions. The OWASP Top 10 for LLM Applications 2025 names the risks, while the trace shows when one is happening. For the wider threat model, see our guide to agentic AI security .
OWASP risk What it looks like Trace signal that exposes it
LLM01:2025 Prompt Injection Hidden instructions in an email or document steer the agent Tool arguments that trace back to retrieved content instead of the user’s request LLM02:2025 Sensitive Information Disclosure Personal or secret data leaks into outputs or logs Detector hits on prompts, outputs or tool results LLM06:2025 Excessive Agency The agent uses a tool or permission beyond its task Write actions outside the allowed tool list, especially with no approval span LLM10:2025 Unbounded Consumption Loops or abuse drive runaway token use Tokens or cost per task far above baseline
Guardrail decisions belong in the trace too. Record each block, each allow and each human approval as its own event. Then you can tell a guardrail that works from one that blocks everything. For the controls themselves, see our guide to AI guardrails .
Traces can also create a risk of their own. A trace that stores full prompts and tool results may hold names, account numbers or API keys.
So redact at capture time, before data leaves the service, rather than cleaning it up in the backend later. Limit who can read raw traces, and log that access as well.
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Use Kanerika’s AI Governance Checklist to confirm access, audit and policy controls before agent traces and actions reach production.
Get the Checklist → How to Implement AI Agent Observability in Five Steps
Rolling out agent observability works best as a short sequence, with a clear exit test for each step. If you skip ahead, you end up with dashboards nobody trusts. The five steps below assume one agent in or near production.
Define tasks and success criteria. Write down each task type and what done means in the target system, so that a script can check it. The business process owner leads, together with the agent team, until every task type has that check.Instrument runs, tools and handoffs. Add OpenTelemetry spans for agent calls, tool calls and handoffs, all carrying a shared task ID. The engineering team owns this step until any random run can be rebuilt from its trace.Add evaluations and system-of-record checks. Add a span that reads the target system after each write, and run scheduled evaluations on a fixed test set. Engineering and QA share it until verified task success is computed daily without manual work.Build dashboards and alerts. Chart the metrics above, then set thresholds from a baseline. The platform or SRE team owns it, and the step is done once every alert has a named owner and a runbook.Feed production failures into regression tests. Turn each confirmed failure trace into a test case. The agent team owns it, and the exit test is met when no prompt, model or tool change ships without passing those tests.
The plumbing under these steps, such as collectors, queues and storage, is part of your wider AI agent infrastructure . Plan for it early, because each task produces many spans rather than one. Teams that already run machine learning operations can reuse their release gates for step 5.
Worked Example: Finding a Silent Failure in an Invoice Agent
The numbers below are illustrative. They show the method rather than report a real deployment, and they follow the invoice agent from the opening across 1,000 runs.
The agent’s own log says 97% of runs succeeded, which is 970 runs. Yet a nightly check against the ERP confirms only 910 of them, or 91%. That leaves 60 runs the agent called done that never landed.
Measure (illustrative) Before the fix After the fix
Runs 1,000 1,000 Reported success 970 (97%) 960 (96%) Verified in the ERP 910 (91%) 955 (95.5%) Silent failures 60 5 Escalated to a person 30 40 Cost per run (assumed) $0.40 $0.42 Cost per verified task $0.44 ($400 ÷ 910) $0.44 ($420 ÷ 955)
Reading One Failed Trace
Next, pull one failed run and read its trace span by span.
Span What the trace shows
invoke_agent invoice-agent Task task_4471 starts from a supplier email about INV-20937 plan invoice-agent Plan: look up the invoice, approve it, then confirm execute_tool get_invoice Argument INV-20931, sourced from session memory execute_tool approve_invoice Returns 200 OK with the message “already approved” ERP read-back (custom span) INV-20937 still pending, so the check fails
The get_invoice span is the root cause, because the agent reused an ID from an earlier conversation instead of the one in the email.
The fix had two parts. First, invoice IDs now come only from the current message. Second, a read-back span confirms the ERP status before the agent reports success. When that check fails, the agent escalates to a person, which is why escalations rise from 30 to 40.
The fix adds two cents per run, about 5%. Cost per verified task still stays at roughly $0.44, while silent failures fall from 60 to 5. Assume each silent failure takes finance 15 minutes to find and fix. Rework then drops from 15 hours per 1,000 runs to about 75 minutes.
Teams building custom AI agents can apply the same read-back pattern to any agent that writes to a business system.
Build, Buy or Use Your Cloud Platform
Once you know what to capture, the next question is where the data goes. There are four broad approaches, and the right one depends on your stack and your team more than on any feature list.
Approach Fits when Watch for
Open-source OpenTelemetry stack You want full control and already run tracing infrastructure You own storage and upgrades, as well as the agent-specific views Agent-native observability platform You want traces, evaluations and prompt history in one place quickly Data residency, export options and how usage is billed APM suite extension Your SRE team already lives in an APM tool Depth of evaluation and agent-specific views Cloud-native option Your agents run on one cloud’s AI platform Coverage of agents and tools hosted elsewhere
Which Approach Fits Your Agents
When every agent runs on one cloud’s AI platform, start with that platform’s native tracing. Agents that span clouds or frameworks fit better on an OpenTelemetry stack or an agent-native platform with OpenTelemetry export. An SRE team that already lives in an APM suite should extend it first, then add agent-specific views where it falls short.
Microsoft Foundry is one example of the cloud-native route. Its observability features , as documented on Microsoft Learn in October 2026, build tracing on OpenTelemetry and integrate with Azure Monitor Application Insights. They also cover LLM calls, tool invocations and agent decisions, with dashboards for tokens, latency, errors and quality.
An AI gateway can also sit in front of model calls and capture them in one place. That helps, but a gateway sees model traffic, not the ERP record your agent changed. So treat it as one data source rather than the whole picture.
Whichever route you take, insist on OpenTelemetry export so you can move later. For named products and trade-offs, see our roundup of AI observability tools and our LLMOps observability platforms compared guide.
What Agent Telemetry Costs to Keep
Agent traces are larger than web traces, because one task produces many spans and each span can carry text. The storage bill follows a simple formula: runs per month × spans per run × average span size × months retained.
Here is an estimate with stated assumptions. Take 200,000 runs a month, 25 spans per run and 4 KB of metadata per span. That is 5 million spans, or about 20 GB a month. If you keep six months, you then hold about 120 GB at steady state.
Storing full prompts and tool payloads changes the math fast. For example, if payloads push the average span to 40 KB, the same workload produces about 200 GB a month. At small scale, sampling may not be worth it. For instance, 10,000 runs a month at 25 spans and 40 KB per span is only about 10 GB. Above that, sampling keeps the bill in check:
Keep 100% of failed, escalated and flagged runs.
Sample a fixed share of healthy runs, such as 10%.
Keep lightweight metadata for every run, even when you sample payloads.
Keep payloads for a shorter period than metadata.
In an OpenTelemetry Collector, the tail sampling processor can express these rules. The sketch below keeps every error trace and every flagged task, then samples 10% of the rest, and app.task.flagged is your own attribute.
processors:
tail_sampling:
decision_wait: 30s
policies:
- name: keep-errors
type: status_code
status_code:
status_codes: [ERROR]
- name: keep-flagged
type: string_attribute
string_attribute:
key: app.task.flagged
values: ["true"]
- name: sample-healthy
type: probabilistic
probabilistic:
sampling_percentage: 10This processor keeps or drops whole traces, so send the per-run metadata you always keep through a separate metrics or logs pipeline.
Suppose 8% of runs are flagged and you sample 10% of the rest. You then keep full payloads for about 17% of runs, which cuts payload storage from about 200 GB to about 34 GB a month. Check your retention obligations before you sample, which the next section covers.
Checklist
Agentic AI Checklist
Work through Kanerika’s Agentic AI Checklist to confirm scope, controls and monitoring before an agent goes live.
Get the Checklist → Observability as Audit Evidence
Agent traces are not only for engineers. When a regulator, auditor or customer asks what an agent did and why, the trace is your record.
The EU AI Act makes this concrete for high-risk systems. Under Article 12 , high-risk AI systems “shall technically allow for the automatic recording of events (logs)” over their lifetime. The AI Act Explorer lists that duty as applying from December 2, 2027 for Annex III systems and August 2, 2028 for Annex I systems.
Article 26(6) asks deployers to keep those automatically generated logs for at least six months, unless other law says otherwise. So a sampling plan has to respect that floor for any system in scope.
In the US, the NIST AI Risk Management Framework is voluntary and was released on January 26, 2023. Its Generative AI Profile, NIST AI 600-1 , followed on July 26, 2024. Both give teams a structure for measuring and managing AI risk, and traces are a practical way to show that work.
Several trace fields tend to serve as evidence. They include timestamps, agent and model versions, as well as input and context IDs. Each tool call with its result also counts, together with guardrail decisions and each human override with who made it.
These records also feed your wider AI governance program, your AI compliance controls and any AI TRiSM reviews. Treat this section as engineering guidance rather than legal advice, and confirm your obligations with counsel.
Common AI Agent Observability Mistakes
The mistakes below are easy to make in a first agent rollout. Each one is cheap to avoid early, but expensive to fix once the agent is live.
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Why AI Agent Projects Fail Before Production?
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Logging prompts only. Prompts and responses show what the model said, but not what the agent did. Without tool arguments and results, you cannot debug an action.Trusting 200 OK. A successful call only proves the API worked. So add a read-back from the system of record before you count a task as done.No task ID across agents. Without one shared ID, a multi-agent run turns into several unrelated logs. Set the ID at the entry point, then pass it everywhere.Unredacted personal data in traces. Traces copy whatever flows through the agent. That is why you redact at capture time and restrict access to raw traces.Dashboards nobody owns. A chart with no owner gets ignored after the first week. So give every dashboard and alert a named owner and a runbook.Alerts before a baseline. Fixed thresholds copied from another system fire constantly or never. Record a baseline first, then alert on deviation.
None of these needs new tooling to fix. Instead, each one needs a decision, an owner and a check that runs without anyone remembering to run it.
How Kanerika Builds Observable AI Agents
Kanerika builds and deploys AI agents for enterprises, and post-launch monitoring is part of the delivery scope. Our agentic AI services cover readiness assessment, agent design, API and data integration, testing against real workloads, staff enablement and post-launch monitoring. Each engagement follows the IMPACT methodology, which sequences the phases so as to reduce deployment risk.
The same service page gives buyers a plain test for any partner: ask about observability and monitoring practices. We think that is the right question, so this guide is our answer to it.
One project shows the habits this guide recommends. A global expert network needed to match specialists to niche survey requests, while poor matches were driving rework and support tickets. The context-aware AI agent Kanerika built validates each shortlist automatically against past participation, survey history and compliance data.
A unified dashboard then shows every recommendation with its context and source links, so reviewers can check the output against the record. The expert-matching case study reports 80% fewer mismatch tickets and 40% higher mapping accuracy. The team also saved 22% of its bandwidth, while the survey lifecycle shrank 34%.
That was not an observability project. Still, the automatic shortlist validation works like a read-back check, and the source-linked dashboard works like a readable trace. Both show what verified outcomes and source-linked outputs look like in production.
Case Study
80% Fewer Mismatch Tickets with Context-Aware AI
An AI agent that validates expert shortlists against participation, survey history and compliance data, with a dashboard showing each recommendation’s context and sources.
Read the Case Study → Before You Put an Agent Into Production
Use this checklist as the last gate before an agent touches real customers or real money. Every row should have a yes and an owner before launch.
Check Ready when
Success defined Each task type has a written, scriptable definition of done Tracing on Agent calls, tool calls and handoffs emit spans with a shared task ID Outcome check A read-back span confirms the system of record after each write Baseline set Two to four weeks of normal ranges are recorded Alerts owned Each alert has a named owner and a runbook Redaction live Personal and secret fields are masked at capture time Retention set Trace and log retention matches your legal and audit needs Regression tests Confirmed failures run as tests before each release
An agent you cannot trace is an agent you cannot trust with real work, however good its demo looks. The core of agent observability is small, and most of it is discipline rather than tooling. That is AI agent observability in one line: trace every step and handoff, measure verified outcomes, and alert only when those outcomes drift from baseline.
So start with one agent and one task type. Define done, add the read-back span and record a baseline before you write a single alert. If you want a second pair of eyes on an agent before launch, talk to Kanerika’s AI team .
Frequently Asked Questions
What is AI agent observability?
AI agent observability is the practice of recording every step an AI agent takes during a task. It captures the plan, each model call, each tool call with its arguments and result, and any handoff to another agent. It also checks the final state in the target system. Teams use it to rebuild any run and confirm the work landed.
How is AI agent observability different from LLM observability?
LLM observability tracks single model calls. It records the prompt, the response, token use and quality scores. AI agent observability covers the whole task an agent performs. That includes planning, tool calls, handoffs between agents and the change in the business system. Teams that let a model act on systems usually need both layers working together.
How do you measure AI agent task success rate?
Divide the tasks confirmed in the system of record by the eligible tasks the agent attempted. The agent’s own report of success does not count. Run the check automatically, such as a nightly job that compares agent actions with ERP or CRM records. Track the gap between reported and verified success, since that gap counts silent failures.
Can AI agent observability detect hallucinations?
Observability helps catch hallucinations by making them traceable. A trace shows which documents were retrieved and what the model produced from them. Evaluations can then score groundedness against that context. Checks against the system of record catch made-up IDs or values. Detection still depends on good evaluations, so treat traces as the evidence those checks use.
How much does AI agent observability cost to run?
Storage cost follows runs per month times spans per run times average span size times months retained. Full prompts and tool payloads can make spans many times larger than metadata alone. Teams control cost by keeping every failed or flagged run, sampling healthy runs, and keeping payloads for a shorter period than metadata.
How do you keep sensitive data out of agent traces?
Redact at capture time, before data leaves the service. Mask personal, financial and secret fields in prompts, tool arguments and results. Store document IDs in place of full retrieved text where possible. Keep full message content switched off unless there is a clear need. Restrict who can read raw traces and log every access to them.
How do I get started with AI agent observability?
Pick one agent and one task type. Write down what done means in the target system. Add OpenTelemetry spans for agent calls, tool calls and handoffs with a shared task ID. Add a read-back check after each write. Run for two to four weeks to record a baseline, then add alerts with named owners.
What metrics should I track for AI agent observability?
Start with verified task success rate, which counts tasks confirmed in the system of record. Add tool-call accuracy, retries per task and cost per successful task. Track p95 step latency, the human-override rate and the policy-violation rate. Watch evaluation scores on a fixed test set after every model, prompt or tool change. Set thresholds from a baseline.
What is OpenTelemetry's role in AI agent observability?
OpenTelemetry gives teams a vendor-neutral format for agent traces. Its GenAI conventions define span types such as create_agent, invoke_agent, plan and execute_tool. Many agent frameworks can emit these spans, so traces can move between tools. The agent conventions are still in Development status. Names and attributes may change, so pin versions and review release notes.
Which tools are best for AI agent observability?
The right tool depends on your stack and team. Options fall into four groups. These are an open-source OpenTelemetry stack, an agent-native observability platform, an extension to an existing APM suite, and a cloud-native option such as Microsoft Foundry tracing. Pick one that exports OpenTelemetry data, then pilot it on real traffic for two weeks.
Why do AI agents need observability if they pass health checks?
Health checks confirm that services respond. They cannot tell whether an agent did the right thing. An agent can get a 200 OK from every tool and still update the wrong record or use stale data. Observability links each action to its arguments and checks the result in the system of record. That is how silent failures surface.
How does AI agent observability work in multi-agent systems?
Every agent stamps one shared task ID on each span it creates. Handoffs between agents are recorded as spans, so the payload passed along is visible. Agents join the parent trace instead of starting a new one. With that in place, a team can follow one task across planner, worker and reviewer agents and find the step that failed.
What is the difference between AI agent monitoring and observability?
Monitoring watches known metrics, such as error rates or latency, and alerts when one leaves its range. Observability records enough detail to rebuild any run, including failures nobody predicted. Evaluation is a third job that scores whether outputs were correct. A production agent needs all three, because each one catches problems the other two miss.
Does AI agent observability add latency to production systems?
The overhead is usually small when it is set up well. OpenTelemetry SDKs batch spans and export them in the background, so the agent does not wait on the backend. Payload capture and redaction add some processing. Measure p95 latency with tracing on and off during a pilot. Then set sampling to keep the cost acceptable.
How do you trace an AI agent's tool calls?
Wrap each tool call in an execute_tool span. Record the tool name, a call ID, the arguments and the result, and mask personal fields first. Add the shared task ID so the call links to the rest of the run. After any write action, add a read-back span that checks the target system confirms the change.
