Last reviewed: May 24, 2026

What is the agentic loop? Definition and pitfall to avoid

The agentic loop is the iterative cycle of an AI agent that acts, observes the result, and decides on the next action, until it reaches its goal or a stopping criterion. Without limits or observability, it can diverge into an infinite loop and consume in a few minutes the equivalent of several days of normal use.

A typical agentic loop chains four phases. Thought: the model reflects on the next step. Action: it invokes a tool. Observation: it receives the result. Decision: it evaluates whether the goal is reached or continues. This structure, inspired by the ReAct paradigm (Yao et al., 2022), is the basis of most agents in production. Three structural risks lurk in any agentic loop. Infinite loop: the agent does not find a stopping criterion and continues indefinitely. Cost drift: each iteration consumes tokens, and a poorly framed agent can multiply consumption by one hundred. Error cascade: an error at step N pollutes all subsequent steps, the agent correcting errors on false premises. Mature deployments impose systematic ceilings: maximum number of iterations, token budget, absolute timeout.

Concrete example

An 80-employee SaaS vendor deploys a technical support agent in June 2025. During a poorly framed update, the agent enters a loop on tickets open for more than 90 days without having a closure criterion. In one night, the agent performs 14,000 iterations on 230 tickets, consuming 8 million tokens, for a cost of 1,100 euros instead of 12 euros on a normal day. The bug is detected in the morning by the API budget monitoring. Fix: ceiling of 5 iterations per ticket, systematic escalation to a human beyond, mandatory audit log. Since then, no similar incident in 11 months.

Pitfall to avoid

The pitfall to avoid with AI agents is not the model's incompetence but the absence of operational guardrails around the loop. Three non-negotiable measures before any production deployment. First, an explicit ceiling on iterations per session (typically 5 to 20 depending on cases), with automatic escalation to a human beyond. Without this ceiling, you expose your infrastructure to documented cost drifts. Second, a per-session token budget, blocking. An agent's inference cost can, in case of bug, exceed the monthly budget in a few hours. Third, granular observability: each thought, each action, each observation must be logged, timestamped, inspectable. 89% of mature companies have implemented agent observability in 2026, because without it, incident diagnosis becomes impossible.

Sources

ReAct: Synergizing Reasoning and Acting in Language Models, Yao et al., arXiv:2210.03629, 2022. https://arxiv.org/abs/2210.03629 (accessed 2026-05-24)
State of Agent Engineering 2026, LangChain (89% agent observability adoption). https://www.langchain.com/state-of-agent-engineering (accessed 2026-05-24)

← Back to glossary

What is the agentic loop? Definition and pitfall to avoid

Concrete example

See also

Further reading

Sources