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🔹 Goal: Define the problem, key use cases, and user requirements for the agentic AI system.

Key Activities:

• Define Objectives: What does the agent need to accomplish?
• Stakeholder Mapping: Identify users, decision-makers, and edge cases.
• Journey Mapping: How does work currently happen? Where can AI automation add value?
• Constraints & Risks: Consider safety, compliance, and bias mitigation.

🎯 Outcome: Clear problem statement, personas, and key user journeys.

🔹 Goal: Design the AI agent's architecture, capabilities, and constraints.

Key Activities:

• Agent Behavior Modeling: Define autonomy level (e.g., advisory vs. fully autonomous).
• Environment & Context: How does the AI perceive, learn, and adapt?
• Multi-Agent Interactions: Will this be a single agent or part of a swarm of AI agents?
• Data & Inputs: What APIs, datasets, or real-time signals does the agent rely on?

🎯 Outcome: Blueprint of the agent's decision-making, interaction flow, and data dependencies.

 🔹 Goal: Prototype a minimal agentic system that executes a small but meaningful task.

Key Activities:

• Low-Fidelity Prototyping: Build a no-code/low-code prototype with rule-based logic.
• Task Automation Mapping: Identify & implement workflow automation using LLMs.
• Ethical AI Design: Include explainability, safety, and intervention triggers.
• Test with Simulated Data: Run early tests with synthetic inputs.

🎯 Outcome: A working prototype of an agent automating a business process.

🔹 Goal: Test real-world scenarios, refine agent behavior, and ensure reliability.

Key Activities:

• User Feedback Sessions: Observe how stakeholders interact with the agent.
• Edge Case Handling: Stress test for unexpected situations (adversarial prompts, hallucinations).
• Performance Metrics: Evaluate accuracy, efficiency, and business impact.
• Safety Nets & Governance: Define escalation procedures for human oversight.

🎯 Outcome: Refined agent behavior with robust fail-safes & improved user experience.