What does an AI agent need to understand about your business before you allow it to make decisions and take action without waiting for human approval?
In this episode of Tech Talks Daily, I speak with Kash Mehdi, Field CTO at Reltio, about the move from analytical AI that supports decisions to agentic AI that can execute them.
Kash argues that leaders should begin treating AI agents as a workforce rather than another collection of software tools. A digital workforce needs training, boundaries, oversight, trusted information, and clear permissions before it can act safely.
He uses the analogy of raising a puppy. When the puppy misbehaves, the problem may be inadequate training or poorly defined boundaries. AI agents present a similar leadership challenge. Organizations must ask what the agent has learned about the business and what authority it has been given.
We discuss why model selection may be receiving too much executive attention. Kash describes four components of an agentic system: the model, tools, data, and context. Models are improving rapidly and tools are increasingly available, but business context remains incomplete across many enterprises.
Data tells an agent a fact. Context helps it understand what the fact means within a particular customer relationship, geography, policy, or business process.
Kash illustrates the difference with a pizza order. The data may confirm that someone is logged in, the model can interpret the request, and a tool can place the order. Context tells the system that it is Friday night, the customer is watching television, and they usually order pineapple and cheese pizza.
The same principle becomes far more serious when an agent is dealing with medical equipment, supply chains, financial customers, or regulated information. It must understand which entities exist, how they relate, what information it may access, and which actions it has authority to complete.
Kash identifies three requirements for safer autonomy: a governed source of truth, a live feedback loop, and enforceable permission boundaries. Trust must be built into the data and operating rules before the agent acts because the familiar human review step may no longer exist.
We also discuss how governance changes when AI can execute decisions at machine speed. A poor decision made by one employee can usually be reviewed and corrected. A poor decision repeated automatically across thousands or millions of transactions can become a business incident before anyone intervenes.
Kash shares examples involving restaurant menu launches, medical equipment deliveries, and call center offers. Each depends on current information and the relationships connecting customers, products, suppliers, locations, and previous interactions.
For CIOs preparing today, Kash recommends building context around reusable entities rather than constructing an isolated data project for every AI use case. He points to Schneider Electric as an example where one unified foundation supported sales, shipping, operations, and marketing use cases.
The conversation ends with a warning about slow data. Autonomous agents need current context because information that arrives after a decision has been made may no longer carry much business value. Kash predicts that the half-life of enterprise data will become a board-level measure.
If a smarter agent can make a poor decision faster and with greater confidence, is your organization investing enough in the context, governance, and feedback needed to keep it on course? Listen to the conversation and share your thoughts with me.
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