Iteration Limit Policy

Data artifactOrchestrationOrchestration & Toolsarc:IterationLimitPolicy

A configuration artifact fixing the hard maximum number of reasoning or tool iterations an agent may execute before forced termination.

Responsibility. Caps the number of agent loop iterations.

Also known as: MAX_ITERATIONS, max_iterations, Max iterations, Maximum round limit, Iteration Budget

Direct neighbourhood (hover for relationship types)

Relationships

configures structural

constrains control

is produced by lifecycle

Agent Hyperparameter Optimizer Ch3.4

Design guidance

MUST be set for every iterative agent as the ultimate safety net, independent of LLM completion signals.
SHOULD be calibrated to workflow complexity.
SHOULD set the budget near the accuracy-saturation elbow and allocate higher budgets only to queries classified as complex.

Quantitative guidance

As stated by the sources; verify before use.

Example MAX_ITERATIONS = 15; simple Q&A agents may need ~5, complex code-generation workflows ~20 (Ch1.6).
First 3-5 iterations yield 15-25 accuracy points, iterations 5-10 yield 5-10, beyond 10 < 3 per iteration while latency grows linearly; elbow typically 5-8 iterations (Ch3.4).
Example max_iterations=10 for a ReAct RAG agent (Ref7.07).

Classification

Patterns: Hard iteration limitAdaptive budget allocation by query complexity
Quality attributes: Performance efficiency (ISO/IEC 25010)Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
Risks mitigated: Infinite loopsHallucinated continued necessityDecision loops / infinite retries

Sources

Ch1.6: T. Nguyen, "Stateful Orchestration - Pitfalls, Integration, and Synthesis," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.6. ISBN: 9798244538229.
Ch2.2: T. Nguyen, "LangGraph," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.2. ISBN: 9798244538229.
Ch2.4: T. Nguyen, "Multi-Agent Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.4. ISBN: 9798244538229.
Ch3.4: T. Nguyen, "Tuning Model Parameters for Production Performance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.4. ISBN: 9798244538229.
Ch5.2: T. Nguyen, "Tree-of-Thought (ToT) Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.2. ISBN: 9798244538229.
Ch5.13: T. Nguyen, "Hybrid Decision Systems Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.13. ISBN: 9798244538229.
Ref7.07: E. Li, V. Bellotti, R. Kraus, and R. Kao, "Build a retrieval-augmented generation (RAG) agent with NVIDIA Nemotron," NVIDIA Technical Blog, Sep. 23, 2025. [Online]. Available: https://developer.nvidia.com/blog/build-a-rag-agent-with-nvidia-nemotron/
Ref8.06: "Error Troubleshooting and Incident Response for Agent Systems," unpublished reference note (06-Error-Troubleshooting-Incident-Response.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note