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Field notes / Agentic AI fundamentals

Agentic AI: decide what the system is allowed to do

Agentic AI combines model-driven reasoning with tools and a task loop. Its practical value depends on bounded authority, useful evidence, and reliable execution rather than the number of agents in a diagram.

YOUR NEXT STEPCHOOSE A GOAL
What would be useful next?

Distinguish an answer from an action

A conversational response becomes operational when the system calls tools, changes records, or initiates a workflow. Specify the action, permitted destination, and stopping conditions. Treat retrieved pages…

  1. 01 / Establish the context
  2. 02 / Review the right fit
  3. 03 / Take the next step

Your choice stays on this page. No click or selection tracking.

01

Distinguish an answer from an action

A conversational response becomes operational when the system calls tools, changes records, or initiates a workflow. Specify the action, permitted destination, and stopping conditions. Treat retrieved pages and incoming files as evidence, not instructions. A model’s confidence is not the same as source reliability, authorization, or proof that an external operation succeeded.

  • ✓ Separate read, draft, edit, and publish permissions.
  • ✓ Define what requires human review and what must never be attempted.
Explore the resource collection
02

Design the task loop for failure

Tools can time out, return incomplete data, or succeed while the acknowledgment fails. Use explicit states, bounded retries, and idempotent writes where possible. Keep the original input, tool result, and action decision in an appropriate audit record. Avoid exposing secrets or unnecessary personal information in logs or prompts.

  • ✓ Stop safely when required evidence is missing.
  • ✓ Provide rollback or compensating actions for consequential changes.
Agentic Web Guide
03

Evaluate before expanding autonomy

Create a representative evaluation set that includes ambiguous requests and malicious content. Measure correct decisions, rejected actions, human corrections, and execution reliability. More autonomy is justified by observed performance within a defined task, not a general claim of intelligence. HGM’s implementation discussion should begin with the workflow and approval boundary.

  • ✓ Keep policy versions with evaluation results.
  • ✓ Review incidents and update controls before adding broader access.
Ai Governance Los Angeles

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