Good observability tooling for AI agents gives you clear answers to three distinct questions: what did the agent spend, what did it do, and did it behave within policy? Conventional APM tools were built around latency, throughput, and error rates for deterministic services — useful signals, but they capture only a fraction of what matters for autonomous agents. Choosing tooling that stops at the infrastructure layer leaves large blind spots in cost, trust, and behavioral compliance. For a broader introduction to the signals worth capturing, see observability for AI agents: logs, metrics, and traces.

Why agent observability is different from service observability

Traditional services are stateless and predictable. You instrument a function, measure its duration, count its errors, and you know what happened. Agents are different in two ways that break this model.

First, agents make decisions. The same prompt can produce different tool calls on different invocations. The interesting question is not whether the agent succeeded — it is whether the decision path it took was reasonable and in-scope. A trace that records only HTTP status codes tells you nothing about whether the agent made a sensible choice.

Second, agents consume resources at machine speed across multiple systems simultaneously. A single agent task can fan out to a dozen LLM calls, several external API calls, and multiple tool invocations — each with its own cost, latency, and risk profile. Rolling those up to a single span duration misses the structure entirely.

Effective observability tooling must be built around the grain of an agent turn, not the grain of a service request.

The three dimensions worth instrumenting

Cost and consumption. Token usage is the most common starting point, but it is only part of the picture. You need to attribute spend to a specific agent, a specific task, and a specific organizational context. Without that attribution, you cannot set meaningful budgets, detect runaway loops, or identify which workflows are unexpectedly expensive. Look for tooling that captures both input and output tokens per call, links them to the originating agent identity, and aggregates to a comparable unit of account.

Behavioral telemetry. This means capturing the sequence of tool calls and model calls that make up a task run, together with enough context to reconstruct what the agent decided to do and why. Concretely: which tools were invoked, in what order, with what parameters (appropriately redacted), and what each returned. This is the record you need when a downstream effect needs to be explained or disputed.

Policy and trust signals. Was the agent operating within its declared scope? Did it attempt to call a tool it was not authorized for? Did any guardrail trigger? These events need to be surfaced as first-class observability signals, not buried in application logs. If your tooling cannot tell you that a guardrail fired on agent X at time T, you will discover policy violations through side effects rather than through monitoring. For context on how guardrails, evals, and monitoring each play a distinct role, see guardrails vs evals vs monitoring.

Criteria for evaluating tooling

Trace granularity and agent-native structure

The fundamental unit in agent traces is the turn: a single model invocation together with the tool calls it produced and their results. Tooling should be able to represent this hierarchically — model call as parent, tool calls as children, with timing, token counts, and outcome at each node. OpenTelemetry provides a vendor-neutral standard for this structure. Tooling that exports OTLP traces lets you route data to any compatible backend and avoids lock-in to a single vendor's proprietary format.

Ask: can the tool represent a multi-step agent run as a structured trace, not just a flat log of events?

Attribution and multi-tenancy

If you run agents on behalf of multiple teams or customers, you need attribution at every level — not just which service generated the span, but which organization, which agent identity, and which workflow triggered the run. This matters for cost allocation, for isolating one tenant's behavioral data from another's, and for incident response (when something goes wrong, you need to narrow the blast radius immediately).

Ask: can every trace, metric, and log line carry organization and agent identity as first-class labels?

Cost and budget integration

Observability and budgeting should be connected, not separate systems. If your observability tooling can export token and cost metrics to a time-series backend (Prometheus, Datadog, CloudWatch), you can set threshold alerts before a budget is exhausted rather than discovering the overrun on the invoice. The key metric shape is a counter or gauge per agent identity, per organization, over a sliding window.

Ask: does the tool expose consumption metrics in a format your existing alerting infrastructure can query?

Behavioral anomaly detection

Raw traces are necessary but not sufficient. You also need the ability to detect patterns that indicate something is wrong: a sudden spike in tool call volume, an agent retrying the same failed action in a loop, a guardrail trigger rate climbing above baseline. Some of this can be built with standard alerting on exported metrics; more sophisticated behavioral analysis requires tooling purpose-built for agent patterns.

Ask: does the tool surface aggregate behavioral signals, or does it leave anomaly detection entirely to the downstream consumer of raw data?

Audit-quality retention and integrity

For regulated use cases, observability data is also audit data. That changes the retention and integrity requirements. Traces that can be silently deleted or modified after the fact are not sufficient for compliance. Look for tooling that supports immutable or append-only storage, or that can export to an audit-specific backend with integrity controls. Chain-of-custody matters: if the trace says an agent did not invoke a particular tool, that claim needs to be credible.

Ask: what are the retention defaults, and can the storage be configured for tamper-evident operation?

Where general-purpose APM falls short

Standard APM platforms — whether hosted or self-managed — handle latency histograms and error counts well. They fall short in several areas that are specific to agents.

LLM-specific semantics are not built in. Token counts, model names, prompt templates, and completion quality signals are not standard span attributes in APM schemas. Some vendors are adding this support, but coverage is uneven.

Policy and trust events are invisible. APM captures what happened at the network level; it has no concept of a guardrail, a content policy, or a trust score threshold. These events live in application logs unless you build explicit instrumentation to surface them.

Cost attribution requires custom work. APM tools can tell you which service generated the most requests, but translating that to token spend and attributing it by tenant requires custom metrics and labeling that most teams have to build themselves.

None of this means general-purpose APM is useless — it remains the right layer for infrastructure health, service latency, and error rates. The gap is at the agent-semantic layer above it.

How Praesidia approaches this layer

Praesidia exports standard Prometheus metrics and OTLP traces so that your existing observability stack can consume agent data without proprietary lock-in. Every metric and trace carries organization and agent identity labels, which means cost attribution and per-tenant analysis come for free rather than requiring custom instrumentation on each workflow.

The platform also surfaces policy events — guardrail triggers, budget threshold crossings, trust score changes — as first-class signals alongside infrastructure metrics. This gives you a single operational view that covers the infrastructure layer (is the service healthy?) and the governance layer (is the agent behaving within policy?) without having to correlate events across separate systems.

For teams building toward audit readiness, the audit log is kept separately from operational telemetry and is designed for integrity and retention rather than query performance. See tamper-evident audit logs with cryptographic proofs for how the underlying integrity controls work, and the platform documentation for configuration details.

Common questions

Do I need a separate observability tool specifically for AI agents, or can I extend my existing APM?

It depends on your scale and risk profile. If you are running a small number of agents with low governance requirements, extending an existing APM platform with custom span attributes and metrics is often sufficient. As agent count, tenant count, and regulatory exposure grow, the gap between what general-purpose APM provides and what you need — policy events, behavioral anomaly detection, tamper-evident audit trails — becomes harder to bridge with customization alone. Most teams find it practical to run both: general-purpose APM for infrastructure health, and an agent-aware layer for behavioral and governance signals.

What should I prioritize instrumenting first?

Start with cost attribution. Token spend that cannot be attributed to an agent identity and an organizational context is the most common source of unpleasant surprises, and it is the signal that most directly drives budget decisions. Once you have reliable cost attribution, add behavioral traces (tool call sequences) and then policy signals (guardrail events, scope violations). This ordering gets you to useful operational insight quickly without requiring full instrumentation up front.

How does observability tooling relate to evals and testing?

Observability is a runtime signal; evals are a pre-deployment signal. They answer different questions. Evals tell you whether an agent behaves correctly on a defined benchmark before you ship it. Observability tells you whether it is behaving correctly in production against real inputs. Both are necessary, and they are complementary rather than substitutes. Runtime observability data can feed back into eval datasets, which is one of the more useful feedback loops to set up once your instrumentation is reliable.