I watched it happen right before my eyes. The AI didn’t just respond to my question—it took initiative. It suggested a course of action I hadn’t asked for, anticipated my next need, and started solving a problem I hadn’t even articulated yet. That moment marked a profound shift in my understanding of where artificial intelligence is heading. We’ve entered the age of agentic AI, and nothing will be the same.
Use Anthropic’s engineering guide to explore workflow and agent designs. Then compare the proposed approach with a simpler process that meets the same need.
Start With the Agentic AI Task
At the heart of this transformation lies a technological breakthrough many people never hear about: embeddings. These mathematical representations translate the messy, nuanced world of human language into precise numerical vectors that machines can process. If you’ve ever wondered how your digital assistant understands your questions or how search engines grasp your intent, embeddings are the answer.
Write down the inputs, required output and stopping conditions. Identify what should happen when information is missing, a tool fails or the request falls outside the agreed scope.
Map Information and Tool Access
Ask which records the system needs to read and which actions it needs to perform. Provide only the access required for the pilot, and document who can approve a change to that access.
Check source freshness, record ownership and the handling of conflicting information. Require the output to show the evidence needed to assess important factual claims.
Define Agentic AI Review Points
Identify decisions that require a person before an action proceeds. Examples for a pilot might include publishing content, sending a message, changing a customer record or committing a budget.
Make these review points part of the implemented workflow. Test whether the system stops when approval is absent and whether the reviewer receives enough context to decide.
Evaluate Controls and Failure Handling
Review the proposal against your organization’s policies and relevant requirements. The NIST AI Risk Management Framework provides a reference for organizing an AI risk review.
Include wrong inputs, unavailable services and ambiguous requests in testing. Record the observed behavior, the recovery path and any work a person must repeat.
Measure an Agentic AI Pilot
Compare the pilot with the current process using a representative task set. Measure acceptable completions, factual errors, review time, operating cost and unresolved exceptions.
Keep the task definitions and scoring criteria consistent. A demonstration on a few selected examples is not enough evidence to claim a broad improvement in business performance.
Expand Only With Reviewed Evidence
Document the pilot’s scope and results before adding more tasks or permissions. Give the service an owner, a review schedule and a clear route for reporting problems.
For help defining a marketing use case and its review process, discuss the workflow with MarketMagnetix.
