📡 Transmission 1: From Workflows to Agents — Introduction to Agentic AI

In product management, we often design workflows that are predictable, repeatable, and efficient. But what happens when the problem isn’t fully known in advance—or when the environment changes midstream?

This is where Agentic AI workflows step in.

🎯 What Makes a Workflow Agentic?

Traditional (non-agentic) systems follow rigid, predetermined steps. They’re reliable but limited.
Agentic workflows, on the other hand, are adaptive processes powered by large language models (LLMs) that can:

  • Revise their thinking and research dynamically
  • Make autonomous decisions at varying degrees of freedom
  • Create or adapt tools on the fly

Think of it as the difference between a scripted chatbot and an agent who can improvise, learn, and act.

⚖️ Degrees of Autonomy

Agentic workflows exist on a spectrum:

  • Less Autonomous → Hard-coded tools, fixed steps
  • Semi-Autonomous → Some decision-making, predefined tool use
  • Highly Autonomous → Generates new tools, adapts in real time

For product leaders, this spectrum is crucial. It’s not about choosing “full autonomy” immediately, but about designing the right level of autonomy for the problem at hand.

🚀 Why Agentic Workflows Matter

  • Performance Boost → Modular design means tools and models can be swapped or upgraded easily.
  • Speed Advantage → Parallelization allows agents to outperform human workflows.
  • Flexibility → Products can evolve from simple text generation to multimodal problem-solving.

Example: Customer Support

  • Non-agentic → Answers FAQs with a fixed script.
  • Agentic → Researches new issues, revises responses, escalates intelligently, and even creates new tools to handle emerging needs.

🛠️ Building Blocks of Agentic Systems

  • Models → LLMs, speech-to-text, image analysis
  • Tools → APIs for search, email, calendar, real-time data
  • Retrieval → Databases, retrieval-augmented generation
  • Code Execution → From calculators to advanced analytics

Together, these components enable workflows that plan, solve, and adapt—even when steps aren’t known ahead of time.

📊 Evaluating Agentic Products

Evaluation is as important as design:

  • Objective → Code-based performance checks
  • Subjective → LLMs as judges of quality
  • End-to-End → Tracing workflows to analyze errors and refine components

📡 Closing Signal

Agentic AI is more than a technical upgrade. It’s a strategic shift in product design.
By embracing autonomy, we move from building features to building adaptive agents inside our products.

📡 Transmission ends here, but the frequency lives on in your product thinking.

📡 Agentic AI Series Navigation

◀️ Next: Mirror, Mirror: The Reflection Pattern in Agentic AI

I’m exploring these ideas through Product Radio — my new experiment in broadcasting product signals.

*— Maharshi Adiraju Product Radio*