Excessive Token Usage: What Happens When AI Agents Enter Recursive Loops
You have already made the case internally for agentic AI. The productivity gains are real, the use cases are compelling, and deployment is either underway or imminent. So this is not a post to convince you that agents are worth the investment. You know that.
What is worth examining more carefully is what happens when an agent runs without the constraints it needs. Not as a theoretical failure mode. As a documented, recurring operational reality that is already affecting enterprise teams who moved quickly and built oversight later.
The specific failure pattern is not an edge case. It is structural. And understanding why it is so difficult to catch with existing tooling is the first step toward preventing it.
The Architecture That Creates the Problem
Every AI agent operates on a core loop: perceive the environment, reason about what to do next, take an action, observe the result, and repeat. That design is what makes agents productive. It is also what makes them capable of running indefinitely when something goes wrong.
Three failure modes generate the most operational and financial damage at enterprise scale.
Recursive loops
Two or more agents enter a feedback cycle where each response triggers the next request, with no exit condition. The agents are behaving exactly as designed. They are simply doing it forever, exchanging messages, consuming tokens, and producing nothing of value. The system does not know it is stuck because, from its perspective, it is not.
Over-querying
An agent misinterprets an error, an ambiguous result, or a missing dependency and retries the same action repeatedly, often with minor variations. To the agent, each attempt is a legitimate new effort. To your API bill, it is a mounting liability that compounds with every iteration.
Unchecked scope expansion
An agent interprets its remit more broadly than intended and begins querying systems, taking actions, or consuming resources well beyond what the workflow required. There is no malfunction to detect. The agent is simply doing more than it was supposed to, and doing it continuously.
What the Evidence Shows
These failure modes are not theoretical. They are documented across production environments, and the pattern is consistent enough to be instructive.
1. The $47,000 Agent Loop
Four agents in a research pipeline entered an infinite conversation loop. Two agents, an Analyzer and a Verifier, ping-ponged requests for eleven days. The team assumed growing costs were organic growth. Final bill: $47,000.
2. The Retry Storm
A data enrichment agent misinterpreted an API error code as an instruction to retry with different parameters. It ran 2.3 million API calls over a weekend. The only mechanism that eventually slowed it down was the external API’s own rate limiter, not any control within the enterprise environment.
3. The Silent Document Agent
A developer deployed a document summarization agent that entered a recursive execution cycle and made 14,000 redundant tool calls before hitting a token quota. The detection mechanism was an external limit, not internal governance.
What these incidents share is not a common framework, model, or use case. They share a common absence: no purpose-built, in-process mechanism to monitor token consumption against productive output and intervene before costs escalated.
Why Standard Monitoring Does Not Catch This
This is the part that catches most teams off guard. The instinct is to assume that existing observability tooling will surface the problem. In practice, it often does not, and the reason is structural.
Traditional infrastructure monitoring tools are designed to detect systems that stop working: crashed processes, connection timeouts, failed health checks. AI agents present a fundamentally different failure pattern. They fail while continuing to work. API calls succeed. Responses are well-formed. Latency metrics look normal. Dashboards show healthy activity.
In the $47,000 multi-agent incident, the team’s monitoring showed no anomalies for eleven days. The agents were exchanging thousands of messages, producing nothing of value, and accumulating costs that were interpreted as normal business growth. The signal was invisible to standard infrastructure metrics because the agents were not malfunctioning in any way those tools are designed to detect. As the post-incident analysis noted, traditional software monitoring detects systems that stop working. AI agents present a unique challenge because they fail while continuing to work.
The same limitation applies to security tooling. Standard network and endpoint monitoring tracks process health and connection state. It does not track whether an agent’s token consumption is proportional to its productive output, whether it has entered a reasoning loop that has become self-sustaining, or whether it is querying systems beyond its intended scope.
Research published in early 2026 found that only 44% of organisations have adopted financial guardrails for AI. That means the majority are running agentic workloads without any systematic mechanism to detect consumption that has decoupled from productive output.
The Gap Between What Agents Need and What Enterprises Have Built
The governance infrastructure that enterprises have built for generative AI, chat interfaces, prompt filtering, output monitoring, is largely designed around a human-in-the-loop model. A user types something, the model responds, a policy checks the interaction. The feedback loop is short. The blast radius of any single interaction is bounded.
Agentic AI operates differently. A single agent invocation can trigger hundreds or thousands of downstream actions, tool calls, and API requests before any human sees an output. By the time an alert surfaces, the cost event may already be complete.
The governance model needs to match the operational model, and for most enterprises right now, it does not. Microsoft’s Cyber Pulse Report found that 80% of Fortune 500 companies now have active AI agents built using low-code or no-code tools, with many of those agents described as unsanctioned, unobserved, or over-privileged. The scale of deployment has outpaced the scale of governance.
What Good Token Governance Actually Looks Like
Addressing the excessive token usage problem requires moving the control point. Standard logging and alerting surfaces what happened after the fact. Token governance operates upstream of that, at the point of consumption, in real time.
Effective governance at the enterprise level requires three capabilities working together. First, the ability to set policy-based token limits at the agent, workflow, and organisational level, so that consumption boundaries are defined before agents run, not reviewed retrospectively after incidents occur. Second, real-time monitoring that can distinguish normal multi-step reasoning from a consumption loop that has become self-sustaining. Third, adaptive safeguards that can automatically throttle, pause, or terminate an agent when its consumption pattern crosses a defined threshold.
Portal26’s Agentic Token Controls were built to close this gap with real-time token governance, policy-based limits at the agent, workflow, or organisational level, and adaptive safeguards that automatically throttle, pause, or terminate excessive token usage before costs spiral. For the first time, enterprises can scale agentic AI with full confidence that consumption stays within defined budgets, and that finance, operations, and security teams all have the visibility to prove it.
This is distinct from output-layer guardrails that filter what agents say or do at the end of a workflow. Token governance operates at the consumption layer, where cost events are generated, and where intervention is most effective.
The Broader Agentic AI Governance Picture
Token governance is one layer of what agentic AI oversight requires. The excessive token usage problem is, in part, a visibility problem: you cannot govern what you cannot see. That starts with knowing which agents are running across your environment, what they are doing, and which carry the highest risk.
The Portal26 AI Adoption Management Platform provides enterprises full visibility and control of all Generative and Agentic AI to enable the buildout of a secure, trusted, and responsible AI program that lifts long-term organisational competitiveness and productivity. As the most mature AI governance offering, Portal26 is the only platform that uniquely provides full-lifecycle management of AI consumption from security to ROI.
Trusted by many, the platform delivers the discovery, risk intelligence, and enforcement capabilities that allow security, finance, and operations teams to govern agentic AI with the same rigour they apply to any other enterprise system. Token controls sit within that broader framework: one component of an end-to-end approach that takes organisations from agent discovery through to verified ROI.
The Control Point Has to Move
The agents running inside your enterprise right now are not waiting for your governance programme to catch up. They are executing workflows, calling tools, consuming tokens, and in some environments entering the kinds of consumption loops that generate significant cost events before anyone notices.
The answer is not to slow down agentic adoption. The answer is to move the control point from reactive alerting to real-time governance, and to build the policy and enforcement layer that lets agents run at scale without the cost risk that currently comes with them.
In Part 2 of this series, we look at what excessive token usage spending means for the business as a whole, and why it is increasingly a finance and operations problem, not just an engineering one.
Book a demo to see Portal26’s Agentic Token Controls in action.