A deep technical walkthrough of designing agent tool loops around per-call cost, latency budgets, and concurrency, using lightweight models like Meta's Muse Ember for high-frequency tool calls instead of frontier model assumptions.
A practical guide to treating agent sandboxes as application runtimes, with clear boundaries for files, commands, network access, credentials, and approval gates.
Explores strategies to fortify LLM-powered agents against malicious prompt injection attacks that could manipulate tool usage.
A practical look at using AI agents for code audit, where they help, where they hallucinate, and why prompt injection and tool scope matter as much as the findings.