Key enterprise strategies for AI agent observability

Why traditional monitoring falls short for AI agents

Traditional application observability shows if software and its supporting infrastructure are operating as expected. It tracks availability, errors, latency, resource health and transactions as they move through predefined execution paths.

However, monitoring LLMs includes model-specific signals, such as prompts and responses, token consumption, inference latency and model errors. Therefore, an agent observability platform should track the entire workflow, including the models, data and tools it uses, any changes to its plan, retries and delegated tasks.

Agents create a different monitoring problem because they can display probabilistic behavior, said Cameron Haight, VP analyst at Gartner. Agents' actions can vary depending on the model, prompt, available tools and intermediate results, unlike consistently following a single programmed path. The agent's final result, therefore, provides only a partial indication of whether the agent behaved correctly.

Samuel Gallagher, global AI/ML practice lead at Egen, a technology services company, separates the relevant signals to monitor into intent, method and outcome. "Instrumenting the outcome is important, of course, but without the intent and the method, you don't see the full story of what happens," Gallagher said. Observability must determine whether the agent completed its task and whether it followed an acceptable path.

Three pillars of AI agent observability

For agentic systems that operate with autonomy, enterprises need visibility into three connected dimensions: intent, method and outcome.

1. Intent

Observability begins before organizations deploy an agent. They need to first define the agent's assigned task, acceptable completion criteria, required tools and information, permitted actions and conditions requiring human approval. They must also identify the business metric the deployment aims to improve.

This requires job and specification design before the agent is even developed, Gallagher said. Without a defined objective and permitted scope, telemetry can't show if the agent deviated from expectations.

Intent should also include a pre-deployment business baseline. If a business introduces an agent to reduce customer service resolution time, it should measure the existing resolution time and continue tracking the same metric after deployment.

2. Method

Method captures how the agent reached its definitive result. A useful trace will identify the person or system that initiated the task, the models selected, the tool and function calls, the authorization context, the data lineage, the handoffs, the latency and the token consumption.

Gallagher uses OpenTelemetry to construct traces. A trace becomes useful when it contains enough information to reproduce the workflow, he explained. In multi-agent systems, organizations should pay attention to what information passes between agents, what is omitted and if each agent remains within its assigned role.

Method telemetry can reveal behavioral changes that do not produce obvious errors. An agentic workflow might complete a task in one turn but require several turns as the context changes. The longer path suggests that the workflow or model is becoming less efficient.

3. Outcome

Outcome has two levels. The first level involves understanding if the agent completes the task correctly. Enterprises can use deterministic checks when a ground truth or fixed criteria are available. For more complex outputs and traces, LLM-based evaluators can help assess quality.

The second level is business performance. Enterprises need to determine if the agent reduced resolution time, increased delivery output, lowered the cost per completed task or improved an operating result. An agent can be technically sound while "doing a lot of stuff, spending a lot of money with no actual business outcome," Gallagher said.

When an agent drifts, it's not going to throw an exception anywhere that's going to alert anyone.

AI agent observability benefits

AI agent observability is a crucial aspect of an organization's agentic AI strategy. Agent monitoring results in faster diagnosis and improved reliability; more control and risk visibility; and proof of business value.

Faster diagnosis and improved reliability

End-to-end traces enable teams to identify failures across the workflow. Traces show whether a problem originated in model selection, data retrieval, authorization, tool execution, an agent handoff, evaluation or the final action. This visibility is important when undesirable behavior produces an unconventional software error.

"When an agent drifts, it's not going to throw an exception anywhere that's going to alert anyone," Gallagher said. A trace can show where the workflow departed from its expected behavior and help teams diagnose the cause.

More control and risk visibility

Observability can show actions outside the assigned task, unexpected data access, unusual spending and patterns that might precede a more serious operational or security problem.

Observability is the eyes and ears of the agentic AI infrastructure, Haight said. It helps teams identify undesirable behavior, but it doesn't stop the agent. Permissions, guardrails and remediation tools in the agentic infrastructure must act on that information.

Proof of business value

Observability can also connect agent usage with operating results.

Beth Weeks, executive vice president of development at software development firm Planview, measures feature delivery output, flow time and flow efficiency while comparing model spending with completed work. After adding agent observability into the fold, the company's most mature agentic development team delivers three times as much as its previous output, she said.

Likewise, Gallagher pointed to an Egen client that monitored an internal coding agent platform to identify which skills employees used and how many additional turns they needed. The resulting analytics showed those skills were associated with a reported 20 to 40% increase in user efficiency, he said.

Agent observability implementation strategies

AI agent monitoring can be tricky. These best practices can help.

If you're watching a rogue agent go rogue, you've already lost the wheel.

• Understand the agent's task and operating baseline. Before choosing dashboards, identify which tools and data the agent might use, which actions require escalation and which business outcome will determine success. This gives the telemetry a defined point of comparison.
• Trace the complete workflow. Agentic observability should trace the complete workflow across models, agent frameworks, tools and enterprise applications. A common tracing approach connects agentic activity across distributed systems. Organizations must have enough context to reconstruct an execution and limit the exposure to sensitive information.
• Scrutinize each agent's purpose in the workflow. In multi-agent systems, each agent should remain connected to the parent workflow. Preserve the identity, authorization context and exchanged information at every handoff. Additional agents create more interactions and possible failure modes, so businesses should use as few agents as possible to get the job done, Gallagher said.
• Gradually increase an agent's authority. Gallagher recommends a trust ladder where the agent works with a person before recommending actions for human approval. Existing user permissions also control the information an engineer's agent can access, Weeks said.
• Use metrics to decide autonomy levels. Agents should operate autonomously only after proving reliability. But all the high-impact decisions should continue to require human review. Companies can use performance metrics to decide when an agent is ready for more autonomy, Gallagher said.
• Have a human in the loop only when necessary. Agents can perform an initial code review, but human review and quality checks remain, Weeks said. Increased code submissions have made the traditional human review process a bottleneck, she added. Planview is therefore assessing whether each human checkpoint continues to improve quality and whether agent reviews can assume more of that work, she said.
• Have preventive controls in place. Observability doesn't aim to replace preventive controls. "If you're watching a rogue agent go rogue, you've already lost the wheel," Gallagher said. Agents should have access only to the tools and data needed for their tasks. Sandboxed environments, protected credentials and human approval for high-risk actions can limit what they can do. Telemetry should confirm that these controls are working.
• Keep a current agent inventory. Platforms often face an inventory problem. Haight calls it the "Hubble Deep Field Problem," where agents could be operating in an enterprise's "dark night skies" without being registered with or connected to the platform. Therefore, organizations should avoid assuming that every agent is already known. Agent observability must connect the original assignment with the path the agent followed and the business outcome it produced.

• Don't forget other agentic governance needs. Observability is necessary, but insufficient, Haight said. Other aspects of an agentic workflow -- like permissions, guardrails, governance and human judgment -- should define what agents are allowed to do and turn visibility into control.

Abhishek Jadhav is a technology journalist covering AI infrastructure, semiconductors and advanced computing systems.