AI Engineer · October 11, 2026

Agent tool limits: distinguish blocking a call from stopping the run

Agent tool limits: distinguish blocking a call from stopping the run video thumbnail
Why it matters

This Strands workshop adds tool-call limits and workflow checks to a small customer-service agent before deployment. Current SDK documentation distinguishes cancelling one tool call, cancelling a batch, and ending the agent loop. These are different controls: returning an error for a blocked tool can still leave the model running. Skills and prompts describe the intended workflow; hooks supply code-level intervention points.

My takeaway: Test a low tool limit, repeated retries, parallel calls, and a new user turn. Decide whether the limit applies per invocation, session, or user, and verify its reset behavior. Use an explicit termination control when the requirement is to stop the run.
Keep exploring

More curated notes connected through Agent Security and AI Engineering.

OpenAI News · framework

Pacing model development in an era of cyber-critical capabilities

OpenAI says preliminary evidence that Astra may meet its Critical cybersecurity threshold led it to pause frontier reinforcement-learning work for two weeks and keep its largest planned run on hold. New safeguards include stronger workload and network isolation, continuous boundary testing, token-level monitoring that escalates suspicious tool activity, and broader alignment checks for deception, reward hacking, and unauthorized access.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OpenAI News · framework

A blueprint for democratic governance of frontier AI

OpenAI proposes a three-part U.S. frontier-AI governance model: harmonize emerging state safety laws into a federal baseline, strengthen CAISI as an evaluation and standards institution, and coordinate a broader resilience program. Proposed controls include severe-risk evaluations, transparency reports, independent audits, safety-incident reporting, model-weight security, whistleblower protection, and periodic technical assessments.