Techniques for Driving AI Agent Requirements

As organizations move beyond the "why" of AI agent adoption to the "what" and "how," they need systematic techniques to identify, analyze, and prioritize opportunities. These techniques help translate broad organizational goals into specific, actionable requirements for AI agent implementation, ensuring that AI agents are built to solve real problems and deliver measurable value.

1. Agentic Application Interaction / Autonomous Application Users

This foundational technique views AI agents not merely as tools to automate isolated functions, but as sophisticated "digital users" or "intelligent actors" that actively perceive, interpret, reason, and interact with existing software applications and their underlying functionalities. Unlike traditional automation that rigidly follows pre-defined scripts, Agentic Application Interaction involves AI agents dynamically understanding application functions (use cases), providing relevant input, guiding workflow progression, making decisions, and even adapting their interactions based on real-time feedback, much like a human user would. This technique defines how an agent acts as an intelligent orchestrator within your existing application landscape.

How it drives requirements:

• It forces a shift from just listing what an application can do to understanding how an agent would intelligently leverage those capabilities, inferring the "intent" behind human use cases and translating it into agent-executable actions.
• This approach highlights scenarios where a human user currently "switches" between applications or performs complex sequences of actions. An agent can then be designed to seamlessly orchestrate these steps, making multiple applications function as a single, cohesive unit from the agent's perspective.
• By viewing agents as users, it requires considering how a human would interpret information displayed by an application, make a decision, and then provide input. This leads to requirements for agents to possess reasoning, context awareness, and even "common sense" to navigate application interfaces.
• The agent doesn't just perform one step; it guides the flow from one step to another, meaning it needs the ability to understand the overall objective, track progress, identify next actions, and validate outcomes across the application landscape.

Key outputs:

• Agent Interaction Models specifying how agents will perceive, process, and act within specific application interfaces.
• Requirements for Application-Agent APIs/Adapters that enable agents to interact with applications intelligently.
• Decision Logic & Reasoning Frameworks for agents to make "user-like" decisions and guide workflows.
• Agent User Journeys illustrating how an AI agent navigates complex tasks across multiple applications.

2. Process Discovery

What it is: This technique focuses on thoroughly understanding and documenting current business processes through traditional methods like workshops, interviews, flow charting, and Business Process Improvement (BPI) or Reengineering (BPR) methodologies. It aims to define the intended or documented flow of work, identify high-level pain points, and conceptualize ideal future states. This helps delineate the macro-level workflow areas where agents could bring transformational value.