Introduction
In the evolving world of digital products, agentic AI products are changing how we define what a “feature” truly is. For decades, product teams have built reactive systems — tools that execute commands, not decisions. But with the rise of large language models (LLMs), that paradigm is shifting.
Instead of waiting for a prompt, agentic systems plan, act, and learn on behalf of users — booking meetings, composing emails, ordering products, or even negotiating outcomes. This new level of autonomy means that features are no longer just functional elements; they are becoming intelligent agents operating inside our products.
Thesis: LLMs are enabling a transformation from reactive features to autonomous agents — and understanding this shift will become a core skill for every product leader and innovator.
What Makes a Feature “Agentic”?
Beyond Automation
Automation executes predefined steps. Agency implies something more: the ability to decide, adapt, and act independently based on goals and context.
An agentic AI product doesn’t just provide recommendations — it acts. For instance:
A productivity app that automatically schedules and confirms your meetings.
An e-commerce assistant that compares offers, checks availability, and purchases for you.
A workflow tool that adjusts itself based on your habits and preferences.
Key Attributes of Agentic Features
Autonomy – The agent can make decisions within defined constraints.
Memory – It remembers past interactions and adapts.
Planning – It breaks down user intent into multi-step actions.
Tool Use – It calls APIs or executes code autonomously.
Self-Correction – It evaluates its own success and retries intelligently.
This level of intelligence means we no longer design interfaces for users to act through, but environments for agents to act within.
How LLMs Enable Agentic Features
Building truly agentic AI products requires several technical capabilities, many of which have only become accessible through modern LLMs.
1. Multi-Step Reasoning and Planning
LLMs like GPT-4 and Claude can chain reasoning steps, break problems into subtasks, and execute complex workflows. This ability forms the backbone of autonomy.
2. Memory and Context Persistence
Through retrieval-augmented generation (RAG) and vector databases, products can now retain user context, allowing agents to make consistent decisions over time.
3. Tool and API Orchestration
By integrating with external APIs — calendars, CRMs, booking systems — agents can move beyond chat to real-world action.
4. Feedback Loops and Self-Improvement
Agentic products collect performance data, track goal outcomes, and use reinforcement-like loops to optimise future behaviour.
Data and Market Signals
According to IBM, agentic AI differs from generative AI in that it “acts and decides”, whereas generative models only “create”.
Gartner projects that over 40% of agentic AI initiatives will be scrapped by 2027 due to unclear ROI — signalling both opportunity and overreach.
Razorfish predicts that “agentic search” will redefine search UX — shifting from showing results to completing tasks on behalf of users.
The agentic AI market is estimated to reach $127B by 2029, growing at a CAGR above 30%.
Agentic behaviour is no longer theoretical — it’s already reshaping how we interact with software
Challenges, Risks, and Ethical Guardrails
As with any emerging technology, product leaders must address serious challenges before scaling autonomy.
1. Hallucinations and Reliability
When an agent acts incorrectly — booking the wrong flight, sending the wrong message — the consequences are immediate. Robust validation layers and human approval loops are essential.
2. Accountability and Governance
Who’s responsible when an AI agent misbehaves — the user, the company, or the model provider? Governance frameworks for responsible AI must extend to autonomous systems.
3. The “Agent Washing” Problem
Just as “AI-powered” became a marketing buzzword, “agentic” risks overuse. Many so-called “agents” are scripted workflows, not true autonomous systems. Leaders must discern between real autonomy and automation in disguise.
4. Technical Overhead
Running persistent context, tool orchestration, and monitoring loops adds complexity. Engineering architectures must evolve — think modular LLM pipelines, vector memory, and stateful APIs.
5. Ethical and Human Oversight
Agentic features must remain accountable. Incorporate:
Human-in-the-loop checkpoints.
Explainability dashboards for audit trails.
Permission boundaries and user consent for actions.
Product Strategy Implications
Rethinking the Product Canvas
Traditional roadmaps track features. Future roadmaps must track behaviours. Ask:
What user goals can be delegated to agents?
What data or APIs does the agent need?
How do we measure the agent’s success?
Measuring Agentic Success
Instead of click rates, measure:
Task completion rate
Time-to-decision reduction
User trust and correction rate
Cost per autonomous action
Governance by Design
Integrate ethical and safety reviews into every sprint. “Ethical sprints” ensure you’re not shipping agency without accountability.
The Future of Product Thinking
The arrival of agentic AI products changes product thinking itself. The role of a Product Manager evolves from specifying interactions to designing intentions.
The product is no longer the interface — the agent is.
Competitive advantage lies not in what your product does, but what your agent knows how to do.
The most valuable features of tomorrow may not have a button — they’ll have initiative.
conclusion
The next era of product innovation will be driven by agency, not algorithms. As LLMs continue to mature, the distinction between a “feature” and an “agent” will blur — and users will increasingly expect digital experiences that act, decide, and adapt on their behalf.
For product leaders, the challenge is clear: start small, experiment with limited-scope agents, and build governance into design from day one. The companies that master this balance between autonomy and accountability will define the next decade of digital transformation.
Key takeaway
Start rethinking your roadmap today: which of your features could — or should — think for themselves?
FAQs
What is an agentic AI product?
A product that uses AI to act autonomously toward goals, without needing constant user prompts.
How is this different from automation?
Automation follows predefined rules. Agentic AI makes context-aware decisions, adapting dynamically.
What technologies enable agentic products?
LLMs, retrieval-augmented memory, tool orchestration, and multi-step reasoning pipelines.
What are the risks?
Unpredictable actions, accountability issues, and overhyped capabilities (“agent washing”).
Should every feature be agentic?
No. Use agency where it adds clear value — focus on high-friction user tasks first.
