As a product manager diving deeper into the world of AI, I’ve been doing what many of us are doing right now: exploring new tools, testing platforms, reading up on agents and agentic workflows, and trying to keep up with the pace of it all.
Along the way, I have noticed something that kept tripping me up. The terms AI tools, AI agents, and agentic workflows kept showing up, often used interchangeably, even in serious conversations.
At first, I brushed it off. But the more I dug in, the clearer it became; these terms actually mean very different things. And understanding those differences isn’t just nice to have, but is essential if we want to make smart decisions, design the right systems, and avoid building on the wrong assumptions.
So, I pulled this together to help anyone…
As a product manager diving deeper into the world of AI, I’ve been doing what many of us are doing right now: exploring new tools, testing platforms, reading up on agents and agentic workflows, and trying to keep up with the pace of it all.
Along the way, I have noticed something that kept tripping me up. The terms AI tools, AI agents, and agentic workflows kept showing up, often used interchangeably, even in serious conversations.
At first, I brushed it off. But the more I dug in, the clearer it became; these terms actually mean very different things. And understanding those differences isn’t just nice to have, but is essential if we want to make smart decisions, design the right systems, and avoid building on the wrong assumptions.
So, I pulled this together to help anyone else who’s on the same path. Whether you’re a product manager experimenting with AI, a GenAI practitioner designing new workflows, or simply trying to cut through the hype, this post is for you. Let’s demystify the terms so we can build smarter, faster, and with purpose.
This post will:
- Define what AI tools, AI agents, and agentic workflows actually are.
- Break down how they differ- both conceptually and in real-world use.
- Show how each is shaping modern product management.
AI Tools: The Rise of Reactive Helpers: Quick Wins, No Strings Attached
AI tools are perfect for one-off tasks. They are synchronous (requiring human input each time) and narrow in their focus (performing one task at a time). These tools shine in tasks like text generation, proofreading, image creation, or summarizing notes. They’re incredibly predictable, delivering consistent results based on well-structured prompts.
Examples: ChatGPT, Perplexity, Claude, Notion AI, Microsoft Copilot, Google Gemini, etc.
Why I Rely on Them
The main draw of these tools is their speed and efficiency. When I need a quick result, whether it’s generating ideas, drafting text, or cleaning up writing, I can rely on AI tools to deliver exactly what I need, fast. They’re powered by pre-trained models like GPT-4 and Claude, and they work with simple interfaces (chat, Web UI, etc.) that don’t require ongoing feedback loops or memory. This makes them incredibly effective for short-term, isolated tasks that don’t require deeper insights or long-term tracking.
I use these tools for tasks that are predictable and don’t need complexity. For instance, when drafting a product feature summary, I’ll use Notion AI to quickly condense large amounts of text into a digestible format. If I need to tag data for analysis, I might use Dovetail AI. These tools are best paired with structured prompts or templates, ensuring consistency and clarity in the results.
AI Agents: Autonomous, Goal-Oriented Collaborators
AI agents aren’t just enhanced tools. They’re autonomous systems designed to pursue a goal, break high-level objectives into steps, execute them across multiple tools, and adjust based on feedback or results. They maintain memory (short or long term), plan multi-steps, and can interface with APIs or tools without constant human oversight.
CategoryAI AgentUsage / Application Language and ProductivityChatGPTConversational assistant for writing, coding, learning, Q&A Jasper AIMarketing copywriting and content generation Coding AgentsGitHub CopilotCode autocompletion and pair programming OpenDevinAutonomous software development and debugging Autonomous Task AgentsAuto-GPTGoal-based task automation using language models BabyAGITask planning and self-directed execution Multi-Agent SystemsMicrosoft AutoGenFramework for collaborative AI agents on complex workflows CassidyProject management and team task coordination Creative and Visual AgentsDALL·E 3Text-to-image generation for creative use Midjourney BotArtistic image generation via Discord-based prompts Virtual Assistants and IoTSiriVoice-activated assistant for mobile and smart home tasks Google AssistantContext-aware assistant for scheduling, commands, and web queries Utility-Based/Real-TimeSelf-driving CarsAutonomous navigation optimizing for safety and efficiency Stock Trading BotsReal-time decision-making for automated financial trading
Table 1: Categories, AI Agents and their usage/applications
Digging deep into these AI agents’ core setup is interesting and gives a good picture of how these work. A planning engine breaks down the tasks, an execution layer runs actions or calls tools, a memory component stores and retrieves context (via vector DBs like Pinecone or Chroma), and an evaluation loop refines actions based upon feedback or results. AI Agent orchestration frameworks like LangChain, ReAct, or CrewAI often orchestrate these actions, helping piece everything together.
Fig 1: Visual Framework: AI Tools Vs AI Agents
Agentic Workflows: Adaptive Systems for Ongoing Operations
Agentic Workflows are not just a set of triggers and rules. They’re structured, multi-step adaptive systems that are powered by one or more AI agents, running asynchronously with self-correction feedback loops. They’re built for context-rich, long-running scenarios across tools. Unlike rigid automation workflows, agentic workflows adapt to chances by observing, reasoning, acting and learning the changes.
Goal → Agent → Plan → Multi-Step Actions → Output → Learn & Adjust → Repeat
Workflow 1: Agentic Workflows Run
| Step | Agent Role | ** AI Agent/Tool** | ** Function** |
| 1 | Log Analysis Agent | OpenDevin / Auto-GPT + logs parser | Reads and interprets application logs |
| 2 | Code Review Agent | GitHub Copilot / GPT-4 | Identifies buggy logic or deprecated syntax |
| 3 | Code Fix Agent | GPT-4 with Tools / Code Interpreter | Generates fixed version of the code |
| 4 | Testing Agent | Test Writer Agent (LangChain tool) | Writes and runs tests based on requirements |
| 5 | Deployment Agent | CI/CD Tool (GitHub Actions + API Agent) | Pushes code to staging or production |
| 6 | Notification Agent | Slack Bot + GPT | Informs dev team with a deployment summary |
Table 2: Example: Software Dev Agentic Workflow
From PMs to Product Orchestrators: How Tools, Agents and Workflows Are Redefining the Role
Product management is undergoing a transformation, not by chance, but by code.
The rise of AI tools, agents, and agentic workflows is reshaping how Product Managers operate. We’re no longer just prioritizing feature requests or managing sprints. We’re building adaptive systems that think, learn, and act, turning PMs into strategic orchestrators of intelligent product ecosystems.
Fig 2: Visual Framework: AI Tools vs. AI Agents vs. Agentic Workflows
Wrapping Up: How AI Tools, Agents, and Workflows Gave Me 10 Hours Back Each Week
Not long ago, I was spending hours sifting through documents, drafting PRDs, coordinating teams, and trying to wrangle data from a dozen tools. Today, that grunt work is handled by a new kind of teammate: AI tools, agents, and workflows that don’t just assist, but amplify what I can do.
AI tools now handle much of my day-to-day cognitive load, like summarizing research and performing analysis in minutes, drafting PRDs, meeting notes and release notes instantly. They give me time back—not to do more admin, but to think more deeply about what matters like our users, strategy, and the impact of our products.
AI Agents are proving to be active collaborators reclaiming hours of mental bandwidth. More importantly, they let me focus on questions AI can’t answer (yet), like What problem are we solving? What should we build next and why?
Agentic workflows are more than dashboards. They’re always-on systems that drive outcomes, learn, adapt, and function like a living, breathing organism.
Through these systems, I find my role and activities redefined. I feel we’re becoming more of a system designer empowered with this support towards building conscious, more meaningful products.
*Disclosure: *All ideas, structure, and content in this article are based on the author’s professional experience in Product Management and technology implementation across various industries, including his research and hands-on experience utilizing AI tools, agents, and workflows. Portions of this article were reviewed using ChatGPT for grammar and phrasing improvements.
**Vivek Sunkara **is a Technology Product Manager at Citi, transforming Risks & Controls data into actionable insights that drive strategic growth. A BCS Member, IEEE Senior Member, IETE Fellow, and ACM professional member, he is an ‘AI-first’ product leader focused on building products and emotionally resonant user experiences.
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Demystifying AI Tools, AI Agents, and Agentic Workflows
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