The AI Agent Uprising: From Assistants to Doers, and the Battle for Control

Artificial Intelligence (AI) is no longer confined to the realm of simple assistance. AI Agents are rapidly evolving, transitioning from passive advisors to proactive doers, decision-takers, and potential replacements for human workers in specific tasks. This shift is driven by advances in Large Language Models (LLMs) and the increasing ability of AI to autonomously plan, reason, and execute complex tasks.

This blog explores the transforming role of AI Agents, how different types of LLMs are powering their evolution, and the implications for users, businesses, and platforms.

                                                          Generated by AI

From Advisors to Doers: The Agentic AI Revolution

Until recently, AI assistants like Siri or Alexa primarily served as “advisors,” providing information, setting reminders, or performing basic tasks based on explicit user commands. Now, AI Agents are becoming increasingly autonomous, capable of:

• Planning and Executing Tasks: AI Agents can now break down complex goals into smaller, manageable steps and execute them independently.
• Making Decisions: AI Agents can use data and reasoning to make decisions without constant human intervention.
• Learning and Adapting: AI Agents can learn from their experiences and adapt their behavior over time, improving their performance and efficiency.
• Memory and context learning over time To make Agents more effective they need to be able to have memory.

The Power of Memory: From Chatbots to Co-Workers

The key to this transformation is the addition of memory to AI Agents. Without memory, an agent resets every turn — it’s like talking to someone with amnesia. Memory allows agents to:

• Recall Past Actions: Remember what they have done previously to avoid repeating mistakes or to build upon previous successes.
• Learn Preferences: Track user preferences and tailor their behavior accordingly.
• Understand Context: Maintain context across multiple interactions, enabling them to handle complex and nuanced conversations.

This ability to remember and learn transforms AI Agents from simple reactive chatbots to proactive co-workers capable of anticipating needs and collaborating effectively.

The Building Blocks of Agentic AI: Diverse LLMs

What’s driving this shift?
A new generation of LLMs, action models, conceptual models — and most importantly, agent memory that makes them context-aware over long timelines.

This is the transformation that will define the next era of AI.

The evolution of AI Agents is fueled by a diverse ecosystem of LLMs, each designed for specific capabilities:

LRMs (Large Reasoning Models): The brain of the agent

• Move beyond text prediction.
• Perform multi-step reasoning, logic, planning.
• Often combined with tool use, search, or retrieval.
• Go beyond prediction to focus on logical reasoning and multi-step problem-solving. They are often integrated with retrieval or tool-use mechanisms for complex queries. Built for multi-step reasoning and complex problem-solving, LRMs excel when agents need more than just next-word prediction. Integrated with retrieval and tool-use, they tackle ambiguous or logic-heavy tasks.

They enable agents to not just respond — but think.

LAMs (Large Action Models): The hands of the agent

• Built for decision-making and real-world execution.
• Can plan tasks, take steps, trigger tools, or operate software.
• Power autonomous workflows, robots, bots, and operations agents.
• Designed for decision-making and real-world actions. They power autonomous systems and agents that can plan, reason, and execute tasks dynamically. These models close the loop, letting agents not just talk, but act — planning, adapting, and executing workflows or purchases in real time.

They enable agents to not just think — but act.

HLMs (Hierarchical Language Models): The organizer of the agent

• Structure reasoning across multiple layers of context.
• Understand user, task, item, and environment simultaneously.
• Enable long-context, multi-perspective responses or recommendations.
• Organize reasoning in structured layers (user-level and item-level understanding). This enables context-aware, multi-perspective responses and recommendations. By structuring reasoning at multiple levels (think: individual users versus group patterns), HLMs empower AI agents to deliver highly relevant, context-sensitive recommendations.

They help agents navigate complexity without losing clarity.

LCMs (Large Concept Models): The intuition of the agent.

• Focus on conceptual understanding and abstraction.
• Map words → concepts → generalized semantic meaning.
• Enable deeper understanding beyond literal interpretation.
• Focus on abstract understanding, mapping words to high-level concepts. They enhance semantic reasoning and conceptual generalization in AI systems. Focused on mapping surface words to deeper ideas, LCMs enable high-level abstraction and conceptual transfer. This is how AI can meaningfully “understand” and generalize across domains.

They help agents “get the idea,” not just the text.

Each model type brings something new to the table — reasoning depth, contextual sophistication, or the ability to actually execute decisions autonomously.

Why Agents Are Becoming More Powerful Than Ever

When you combine all four:

🧠 LRM (think)
 🖐️ LAM (act)
 🧩 HLM (organize)
 🌐 LCM (understand concepts)
 💾 Memory (learn over time)

You get something radically new:

⭐ An AI that doesn’t just respond — it operates.

⭐ An AI that doesn’t wait for instructions — it takes initiative.

This is why modern agents are stepping into new, transformative roles:

• task executors
• workflow orchestrators
• decision-takers
• autonomous business operators

Not replacing humans — but replacing repetitive human effort.

A Shifting Landscape: The Battle for Control of the Shopping Journey

The increasing capabilities of AI Agents are disrupting established business models, particularly in the realm of e-commerce. For example, Perplexity’s Comet, an AI Agent, isn’t just fetching product info — it’s trying to complete transactions. This raises concerns for platforms like Amazon, which rely on data and control over the customer journey.

Agents in Action: From Advisors to Actors

Until now, most AI “assistants” were advisors — offering suggestions, but leaving the real work (purchasing, scheduling, follow-through) to humans. Not anymore. Today’s AI agents strive to get things done.

Amazon is so concerned with the growth of these doer like AI, to ensure proper environment. But what is the truth on behind to take action?

• The need to have a more trust and compliance as every new tool appears.
• They’re becoming “doers.” (Automate what they needed)
• Mimicking of human (They’re starting to understand and do everything what human did!)
• They might be doing unethical acts or go out of line and become rouge AI or create any other type of issue.

When an AI Agent mediates a purchase, Amazon loses:

• Data Visibility: No personalization or upsell opportunities.
• Customer Relationship: The “agent” becomes the brand.
• Control: Agents could redirect traffic to rivals.
• Safety: If any mishap happens AI becomes not just responsible and also accountable to ensure and solve issues.

In response, platforms are reinforcing their walls:

• Google blocked AI browser scraping.
• OpenAI introduced a controlled retrieval API.
• Amazon now says: You can’t automate transactions without permission.

This is becoming a challenge and will increase in complexity.

Platform Power vs. AI Intermediation: The Design Dilemma

The emergence of Agentic AI raises tough design questions:

• How do we ensure traceable, compliant actions by autonomous agents?
• Should platforms expose secure agent APIs instead of banning them?
• Can commerce systems authenticate machine customers safely?

By 2028, it’s predicted nearly a third of online purchases will be initiated (or heavily influenced) by AI assistants. For platforms like Amazon, this is an existential change. When an AI agent intermediates the purchase:

• Data visibility drops: the platform loses raw insights for personalization and upsell. No personalization → no product recommendations → no upsell.
• Customer Relationship: The brand becomes the AI agent — not Amazon.
• Commerce Control: Agents can reroute buyers to competitors instantly.
• The “agent” becomes the brand — the user’s loyalty shifts from retailer to AI.
• Control over payments and traffic weakens — agents could route buying decisions to competitors.

This is why platforms are drawing boundaries:

• Google blocks agent-based scraping.
• Amazon disallows autonomous transactions without permission.
• OpenAI provides controlled retrieval APIs.

It’s the new rule of the digital era:

“No API, No Entry.”

This threat isn’t limited to retail. Google blocks browser scraping by AI tools, OpenAI restricts in-browser retrieval, and Amazon outright prohibits automation of transactions by external agents. The message: Play by our API rules, or you’re out.

These questions are not just technical, they have economic and strategic implications.

The Technical Crossroads: Navigating the Future of AI

Agentic AI is more than just a technological advancement; it’s a paradigm shift:

From Text Generation to Decision Execution

From “generate text” ➜ to “perform actions” ➜ to “execute decisions.”

The world of AI agents is quickly shifting, shaped by advances in reasoning, memory, and agency. We’ve crossed the line from “generate text” to “perform actions” to, soon, “execute decisions.” As agentic AI becomes more capable and influential, expect power struggles, new technical architectures, and a fundamental rethink of how humans, platforms, and machines collaborate.

To navigate this new landscape successfully, we need to:

• Develop secure and robust agent APIs that enable AI Agents to interact with platforms in a controlled and transparent manner.
• Establish clear ethical guidelines and regulatory frameworks to govern the use of AI Agents.
• Foster collaboration between AI developers, platforms, and policymakers to ensure that AI Agents are used responsibly and for the benefit of all.

Why This Moment Matters: Agentic AI Raises New Questions

As agents shift from generating text → to performing actions → to making decisions, we face critical system-level questions:

🔒 How do we ensure safe, compliant, auditable agent actions?

🔌 Should platforms provide secure agent APIs instead of banning them?

🪪 How do we authenticate machine customers?

🏗️ What architecture ensures traceability and accountability?

These are no longer futuristic concerns — they’re design requirements for the next generation of AI systems.

Design Challenges at the AI Frontier

Allowing autonomous agents to act on users’ behalf raises tough questions for both builders and platforms:

• How do we verify that agents act transparently and comply with regulations?
• Should companies expose secure agent APIs to guide automation, or just ban it outright?
• Can commerce systems safely authenticate and audit “machine customers”?

These are no longer niche debates — they’re central to how we’ll interact with the digital economy as AI grows up.

The Endgame: From Tools → Teammates → Autonomous Operators

The real trajectory of AI agents:

1. Assistants (reactive): Help answer questions.

2. Co-pilots (interactive): Help perform tasks.

3. Doers (operative): Execute tasks autonomously.

4. Decision Systems (strategic): Make informed decisions based on goals.

5. Autonomous Operators (self-directed): Run workflows and adapt without ongoing human supervision.

And the catalyst behind this evolution?

👉 Memory
 👉 Multi-model agent architectures
 👉 Specialized LLM types (LRM, LAM, HLM, LCM)
 👉 Tool-use
 👉 Autonomous decision-making

This is not the future — it’s happening now.

Conclusion: The Dawn of the Autonomous Agent

AI Agents are poised to revolutionize the way we live, work, and interact with the world. As they continue to evolve, it’s crucial to address the technical, ethical, and societal challenges they present. By embracing a collaborative and responsible approach, we can harness the full potential of AI Agents to create a more efficient, productive, and equitable future. The digital walls must come down and open doors should always work

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