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.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
