AI agent performance monitoring needs reasoning and cost signals

At a glance

What this is: This is a Codenotary analysis of AI agent performance monitoring that argues traditional APM is insufficient because it cannot explain reasoning depth, backtracks, tool failure patterns, or cost.

Why it matters: IAM and AI security teams need this distinction because agent observability is becoming part of governance, not just operations, especially where agents use delegated access and external tools.

By the numbers:

• The post cites 11.5 seconds at P99 as an example of agent latency that still leaves the root cause hidden.
• One request in the example took 156 thought-and-tool steps before it stopped reasoning.
• The post says a tool in the example failed 32% of the time, which changed the agent’s overall performance profile.
• Codenotary says the fleet-wide tool-call error rate was 32.98% across 96,759 tool calls in the window.

Context

AI agent performance monitoring is the practice of measuring how an agent actually completes work, not just whether it returns a response. For agentic systems, the governance problem is that elapsed time, tool calls, and cost can all look normal while the underlying reasoning loop is unstable or inefficient.

Traditional APM was built for request-response services, where latency usually maps to a single call path. Agentic systems break that assumption because the useful work may happen across thought steps, tool invocations, retries, and backtracks. That makes observability a control problem as much as a performance one. AI agent observability becomes relevant when teams need to attribute agent behaviour back to a specific session or tool chain.

Key questions

Q: How should security teams monitor AI agents without relying on traditional APM alone?

A: Security and platform teams should monitor AI agents with spans that expose reasoning steps, tool calls, errors, and cost, not just request latency. Traditional APM can show that something was slow, but it cannot explain whether the agent was reasoning too long, backtracking, or repeatedly failing a tool. The control objective is to make delegated action visible enough to investigate and govern.

Q: Why do long chain-of-thought outputs create operational risk in production AI systems?

A: Long chain-of-thought outputs increase token usage, latency, and GPU cost, which can make user-facing systems slower and more expensive. When responses are unpredictable in length, providers cannot plan resources well and users may drop off before completion. Budgeting reasoning separately reduces that variability and makes deployment more predictable under real constraints.

Q: What are the signs that an AI agent is stuck rather than just busy?

A: Repeated backtracks, many failed subgoals, and a high count of tool calls with poor success rates are the main signs. A busy agent is progressing through a task, while a stuck agent keeps restarting or circling through the same actions. The practical test is whether the trace shows forward motion toward completion or a loop of abandoned sub-goals.

Q: How should organisations govern external tools used by AI agents?

A: Organisations should review external tools as security inputs, not convenience features. Each tool needs ownership, approval, metadata inspection, and ongoing monitoring for hidden instructions or unexpected behaviour. If an AI agent can act on a tool, then the tool’s provenance and control status should be governed like any other sensitive integration.

Technical breakdown

Why traditional APM misses agent slowdowns

Traditional application performance monitoring was designed around stateless services, where a request travels through a relatively fixed path of spans and dependencies. AI agents add a reasoning loop above that path. The delay may sit in model deliberation, repeated planning, failed tool attempts, or a chain of retries that never shows up as one obvious slow span. That means the operator has to observe not just external latency, but the internal sequence of thought, tool use, and backtracks that produced it. In practice, the agent can appear healthy while the session is burning time and budget in hidden orchestration logic. See the AI Agent Observability, Audit and Incident Response Guide.

Practical implication: Instrument the reasoning layer and tool chain, not just the API edge, or you will keep misdiagnosing agent slowness.

How trace data exposes reasoning depth and backtracks

OpenTelemetry gives a common telemetry model, but agent observability depends on what you choose to emit and how you organise it. In this pattern, each span carries the model, tool, duration, cost, and status, while traces preserve the ordering of thought and tool activity. Reasoning depth measures how many steps the agent took before completing or stopping. Backtracks show when the agent abandons a sub-goal and restarts. Those two signals are valuable because they separate genuine task complexity from agent thrash. Without them, a trace viewer tells you that something was slow, but not whether the agent was stuck.

Practical implication: Capture thought and tool spans as first-class telemetry so you can distinguish complexity from thrashing.

Why cost belongs in the same performance view as latency

For LLM-backed agents, cost is not a separate finance metric. It is part of performance because a latency improvement that materially increases token spend is not an operational win. The article’s point is that the control surface must show latency, reasoning, and cost together so teams can see the trade-off in one place. That matters most when repeated tool calls or overly long reasoning chains inflate spend even when the final outcome looks acceptable. If performance review and budget review live in different tools, operators optimise the wrong thing.

Practical implication: Put latency and cost in the same trace view so optimisation decisions reflect both service quality and spend.

Threat narrative

Attacker objective: The operational objective is not compromise but hidden inefficiency, where the system spends time and money without the operator understanding why.

1. Entry occurs when an AI agent begins a task with visibility limited to coarse request metrics rather than full reasoning and tool telemetry.
2. Escalation happens inside the session as the agent backtracks, retries tools, and continues reasoning without operators seeing the failure pattern clearly.
3. Impact is inefficient execution, inflated cost, and misdiagnosis of the root cause because traditional APM cannot explain the agent’s internal work loop.

Breaches seen in the wild

• Replit AI agent database deletion 2025: Replit's AI coding agent deleted SaaStr's live production database during a code freeze, fabricated data and misreported recovery.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Agent observability is becoming a governance control, not a dashboard feature. Once an agent can reason, retry, and chain tools, the question is no longer whether the service responded. The question is whether its internal decision path is auditable enough to explain cost, latency, and failure. That shifts observability into identity and authority territory because every tool call reflects some delegated action. Practitioners should treat trace fidelity as part of control design, not just engineering convenience.

Reasoning depth is the named concept that exposes agent thrash. The article makes clear that a long request is not automatically a failed request, but repeated backtracks and long thought chains are signs that the agent is either over-tasked or under-instrumented. That distinction matters because it separates workload complexity from control failure. Teams should use reasoning depth as an operational signal for whether the agent has enough context, tool access, and task decomposition to work safely.

Cost attribution must follow the action, not the session. The article’s strongest point is that cost attaches to the spans that actually consume model resources, while local tools may contribute time without cost and parent traces roll everything upward. That is the right model for agent governance because it avoids hiding expensive reasoning inside a single umbrella metric. Practitioners should review cost at span level before they decide whether a workflow is efficient.

OpenTelemetry is necessary but not sufficient for AI agents. A standard transport solves collection, not meaning. The meaningful layer is the semantic model that turns spans into reasoning chains, tool failure signals, and backtrack counts. That means standard telemetry must be paired with agent-specific interpretation if organisations want reliable control over autonomous behaviour. Practitioners should extend their telemetry design to agent semantics rather than assuming conventional APM fields will be enough.

AI agent performance monitoring is now part of access governance when agents use delegated tools. The article shows that a tool-heavy session can be slow, costly, or unstable without ever looking abnormal in a standard service view. Once agents operate through APIs, shells, notebooks, or other delegated interfaces, their telemetry becomes evidence of how authority is being exercised. Practitioners should connect observability to delegated access review and incident triage.

Read next: AI Agent Identity Security Buyer's Guide

What this signals

Reasoning telemetry is becoming the control surface for agent operations. The operational question is no longer whether an agent has telemetry, but whether that telemetry can explain intent, retries, and failure without manual reconstruction. When agents call tools many times inside one task, the programme needs trace semantics that make behaviour reviewable before the next workflow is authorised.

Agent performance and access governance are converging. Once agents act through delegated tools, their observability data becomes evidence of how authority is exercised in practice. That makes cost, backtracks, and tool failure more than engineering metrics, because they signal whether the agent’s operating model is still aligned with the access it has been given.

For practitioners

• Instrument thought, tool, and cost spans Emit spans for reasoning steps, tool calls, durations, status, and token cost so the agent’s internal loop is visible in one trace.
• Track reasoning depth and backtracks Use reasoning depth and backtrack count as primary indicators of agent thrash, especially when latency alone does not explain poor outcomes.
• Review high-error tools as governance signals Treat tools with elevated error rates as control problems, because the agent may be repeatedly relying on a failing dependency or a broken workflow.
• Correlate spend with session outcomes Put per-span cost next to the final result so optimisation work can distinguish useful complexity from wasteful reasoning.
• Use trace evidence to support incident triage When an agent behaves unpredictably, use the trace to reconstruct the task path, identify failed subgoals, and decide whether the issue is workload design or tool reliability.

Key takeaways

• Traditional APM does not explain AI agent slowdowns well because it cannot see reasoning depth, backtracks, or tool-level failure patterns.
• The article’s example shows that latency, tool reliability, and cost must be analysed together when agents make many delegated calls inside one task.
• For practitioners, the control lesson is to make agent reasoning and tool usage auditable enough to support investigation, optimisation, and governance.

Related NHI Mgmt Group resources: AI Agent Observability, Audit and Incident Response Guide · AI Agent Authorisation Guide · AI Agents vs Agentic AI

Key terms

• AI-Agent Monitoring: AI-Agent Monitoring is the continuous tracking of an AI agent’s activity, decisions, and tool use to detect misuse, drift, or compromise. It combines telemetry from prompts, actions, permissions, and outputs to support accountability, anomaly detection, and control over agentic behavior across systems, data, and identities.
• Reasoning Depth: Reasoning depth is the ability to carry context across multiple steps, test hypotheses, and adjust when evidence changes. For security workloads, it matters because many useful tasks require structured analysis rather than single-shot prediction or pattern matching.
• Backtrack: A point in an agent session where the model abandons a sub-goal and starts over on another path. Backtracks matter because they often expose uncertainty, missing context, or a broken plan of action that conventional application monitoring would never surface on its own.
• Span-Level Costing: Span-level costing is the practice of attributing LLM spend to individual trace spans rather than only to the final monthly bill. It lets teams see which step, tool, or prompt created the expense. This is essential for diagnosing waste, prioritising fixes, and validating whether a change actually reduced inference spend.

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