LLMs are probabilistic, but production systems still need deterministic guarantees. Improving your prompt may raise the odds but it cannot make the rule absolute and that's where you need a deterministic approach.
Using lessons from my Agentic AI project [https://github.com/msalanci/logs_talk_to_me/tree/v3] - a conversational agent investigating AWS logs using SQL queries and AWS API calls - this talk explores how deterministic hooks let developers intercept and control the agent while its reasoning loop is still running.
The focus is not an API tour, it's the broader engineering pattern: placing deterministic logic at agents' specific lifecycle events and how those hooks can inspect, modify, cancel, and retry model-generated actions before they affect.
Actions like return a correction message to the model, let the agent repair its own request, rewrite SQL queries, maintain state across tool calls, suppress wasteful retries, validate final answers, inject application context and more... all that can be provided by a deterministic hooks, assigned to an agent during the right event.
Finally, we will compare deterministic hooks with "LLM as judge" and establish a practical rule for choosing between them.

talk for 30 minutes, but I have enough material to extend it to 45.
No special technical requirement needed