Cognitive architectures and building AI agents that do work

When I first started developing AI agents, the most impressive feature of LLMs was their ability to converse. But over time, I began to see their limitations. While they could generate thoughtful responses, they weren’t doing much. These chatbots weren’t true agents—they didn’t take meaningful action, execute tasks autonomously, or work on behalf of the user. That’s when I realized the real potential lies in cognitive architectures, designed to enable AI agents to become agentic—tools that actively execute and achieve outcomes.

This shift from conversational capabilities to agentic action represents the next frontier in AI development.

Beyond chatbots: the need for advanced architectures

Today’s LLMs, like ChatGPT and Claude, are great at mimicking conversations. But they fall short when it comes to doing actual work. The illusion of stagnation with these models arises from their limited ability to take initiative, handle ongoing tasks, or integrate seamlessly into workflows.

The need for more sophisticated cognitive architectures is clear. These architectures go beyond generating text; they empower LLMs to act as agents that can plan, execute, and adapt in real time. Whether it’s automating business processes, managing projects, or executing technical tasks, cognitive architectures can turn LLMs into true workers—not just passive assistants.

Components of cognitive architectures

Through my research and experimentation, I’ve come to understand that several key components are essential to transform LLMs into agentic systems capable of real-world tasks:

• External data integration and real-time information retrieval: Imagine an LLM that could pull in live data, interpret it, and adjust its responses accordingly. This could be a game-changer in industries like finance, health, and research, where real-time decision-making is critical.

• Personalization and user context awareness: Many current systems treat each interaction as isolated. Cognitive architectures can allow models to understand who they are talking to and tailor responses based on past conversations, user preferences, and specific contexts.

• Business context integration: For AI to truly drive value in business, it needs to understand the broader context of the industry, company goals, and operational specifics. Integrating business-specific knowledge directly into the AI’s cognitive framework could lead to better, more actionable insights.

• Tool and API calling capabilities: Imagine working with an AI that can seamlessly execute other machine learning models, call APIs, or even manage software workflows. This level of capability opens up new avenues for automation, innovation, and collaboration.

• Long-term memory and context management: Current models can lose track of context when a conversation extends too long. Advanced cognitive architectures can store and retrieve information from long-term memory, making interactions feel more coherent and insightful over time.

• Reflection and self-refinement: In my work, I’ve noticed that even the best AI agents sometimes provide incomplete or inaccurate answers. But what if LLMs could reflect on answers and improve them, all before responding to the user? Cognitive architectures could allow for this kind of self-improvement, leading to more robust and reliable systems.

Implementing cognitive architectures

Creating these agentic systems isn’t just about technology—it’s about making AI that works for people and businesses. Through my own efforts, I’ve learned that implementing these systems requires careful balancing of complexity and utility. AI agents must be intelligent enough to handle nuanced tasks while remaining efficient and reliable.

• Designing for business needs: Whether you’re automating repetitive tasks or building agents that contribute to creative work, cognitive architectures should be tailored to specific business needs. These systems could power anything from marketing campaigns to legal research, acting as specialized workers who execute with precision.

• Integration with existing systems: Cognitive architectures need to mesh seamlessly with current technologies. Whether working with APIs, enterprise systems, or cloud services, the key is making AI agents part of a larger operational ecosystem.

The future of LLM interactions: action-oriented AI

We are already seeing the beginnings of this shift toward agentic AI in new user interfaces and functionalities. For example, tools like Claude’s artifacts or ChatGPT with its canvas are enabling users to interact more dynamically with AI systems. But this is just the start. Soon, we’ll see AI agents that can autonomously iterate on projects, refine assets, and even work collaboratively with human teams to deliver concrete results.

As these architectures mature, the potential for LLMs to take on more sophisticated, action-oriented roles will expand. AI agents will be capable of handling diverse tasks—from automating HR workflows to managing complex engineering projects. This evolution toward agents who can manage, execute, and refine tasks autonomously will redefine how we interact with AI.

And these agentic systems won’t just assist—they will be doers who take initiative, handle complex workflows, and produce real-world results.

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The shift from conversational LLMs to true agentic AI represents an exciting turning point. Cognitive architectures are the key to unlocking this potential, enabling AI to act on behalf of users in real, meaningful ways. For developers and businesses, now is the time to explore how agentic AI can transform workflows, automate processes, and drive innovation across industries.

The next wave of AI won’t just talk—it will act, pushing the boundaries of what we thought possible in AI interactions and business applications.