Handling long-running AI agents: timeouts, checkpoints and partial progress

An agentic task that runs for minutes needs a different failure model than a single API call. Timeouts, checkpointing and resuming partial work, done right.

Written by

Pranjul Rathour

GenAI Engineer at SCULT INDIA, based in Kanpur, India. Builds production RAG systems, fine-tunes open LLMs and ships agentic AI products; has mentored 200+ college developers. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks.

pranjulrathour41@gmail.com LinkedIn GitHub X Instagram Bluesky Dev.to

Walking a room through evaluation criteria

An agentic task that plans, calls tools, and iterates over several minutes fails differently than a single request-response API call — and needs a different reliability model to match.

What to build in from the start

A hard overall timeout, so a stuck agent doesn't run indefinitely and silently consume budget.
Checkpointing progress after each meaningful step, so a failure partway through doesn't lose all prior work.
A way to resume from the last checkpoint rather than restarting the entire task from scratch after a transient failure.

The failure mode unique to agents

An agent can get stuck in a loop — repeating a failed tool call, or oscillating between two plans — without ever technically 'crashing'. Detect this with a step-count or repeated-action limit, not just a wall-clock timeout, since a looping agent can burn a lot of budget well within a generous time limit.

See agentic AI patterns: tools, memory, guardrails.

— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.