When an AI agent produces the wrong result, blaming the model is easy. But many apparent model failures begin outside the model.

An agent can be viewed as a black box: it receives an input and produces an output. Around that box, teams still control three critical elements.

The specification defines what the agent must do. The context makes that task correct for a particular user, company, system, and moment. The validation method determines whether the polished-looking output is actually right.

Each can fail independently. A request to update a page “title,” for example, may refer to the visible H1 or the browser title. The agent can make a perfectly reasonable choice while solving the wrong problem.

Using practical examples and a demonstration, this session introduces a framework for writing less ambiguous specifications, identifying the context an agent truly needs, and validating its work independently of how convincing the result appears.

Target audience: AI engineers, software engineers, product engineers, tech leads, and teams designing agent workflows.
Level: Intermediate.
Preferred duration: 30–45 minutes.
Format: Framework-based session with examples and a live or recorded demonstration.
Prerequisites: Basic experience using LLMs in development or product workflows.
Source: Lessons from building and reviewing production AI systems.