I’ve been the biggest fan of AI Agents and have been building solutions around them for over a year now. Recently, the release of reasoning models like o3 and DeepSeek R1 and applications on top of them like Deep Research made me think that general purpose agents are within reach with tools.

Here is how I see the evolution of AI Agents that can do everything:

  1. Simple LLMs = GPT 3.5
  2. Powerful LLMs = GPT 4o
  3. Combine LLMs with reasoning capabilities, you get GPT o1, o3, DeepSeek R1
  4. Give models with reasoning capabilities tools, you get DeepSeek Research
  5. Give models who can research a coding execution and deployment, you get a general purpose agent?

Here is a minimal single Agent architecture I’m thinking that can make a general purpose agent a reality:

  1. Reasoning model (GPT o1, o3, DeepSeek R1, or Deep Research)
  2. Search Module
  3. Code Generation & Deployment Module
  4. Communication & Interaction Module
  5. Memory & Self-Reflection Module
  6. Self-Improvement & Tool Creation Module

I think these fundamental components are enough to make a general purpose agent. Is foundationally this all it needs to create a self improving, general purpose agent that can learn to do anything?

We are of course masking a range of complex, real world issues like ensuring robust error handling, managing secure API integrations, and maintaining a unified context across diverse tools. Automatically generated code must be rigorously tested and validated to handle unexpected edge cases, such as API changes or ambiguous documentation, while also securely managing sensitive credentials, tokens, and rate limits. Moreover, coordinating multiple interfaces—from search and code generation to deployment and communication—introduces significant integration complexity. As the system continuously evolves and self-improves, it must balance new capabilities with the stability of established functions, necessitating robust safety mechanisms and continuous evaluation through reinforcement learning or human-in-the-loop feedback. This highlights that our simplified assumptions are just the tip of the iceberg when it comes to real-world implementation challenges.

On the other hand there are also many exciting architecture patterns like mixture of experts, multi-agent systems, mixture of models, computer use agents, etc., that can help fill the gaps and improve the reliability of a general-purpose agent.

Here is a formal breakdown of the components:

  1. Reasoning Engine

Purpose: Acts as the “brain” of the agent, using advanced LLMs (such as o1, o3, DeepSeek R1, or Deep Research) to understand tasks, plan multi-step actions, and determine which tools to invoke. Responsibilities: Decomposing complex tasks into smaller, manageable subtasks. Deciding when to generate new tools or use existing ones. Orchestrating overall workflow by interacting with other modules.

  1. Search Module

Purpose: Retrieves necessary information such as API documentation, tool descriptions, or context from external sources. Responsibilities: Querying web resources or internal databases. Validating and fetching up-to-date documentation. Assisting the reasoning engine by providing contextual information for tool development.

  1. Code Generation & Deployment Module

Purpose: Automatically writes, tests, and deploys code to interact with external APIs or to perform specific tasks. Responsibilities: Parsing documentation and generating reliable code. Running tests (unit, integration, simulation) to verify the code’s robustness. Deploying the code as a callable tool within the agent’s ecosystem.

  1. Communication & Interaction Module

Purpose: Manages outbound and inbound communications—whether sending messages, making calls, or interfacing with human users. Responsibilities: Integrating with messaging services (e.g., SMS, email, chat platforms). Crafting and dispatching context-aware messages. Handling responses and maintaining dialogue where necessary. Ensuring security (e.g., encryption, authentication) when communicating sensitive data.

  1. Memory & Self-Reflection Module

Purpose: Stores the agent’s internal state, previous interactions, successes, failures, and feedback, enabling it to learn and adapt over time. Responsibilities: Logging activities and outcomes. Maintaining context for ongoing tasks. Providing historical data to the reasoning engine for improved decision-making.

  1. Self-Improvement & Tool Creation Module

Purpose: Enables the agent to assess its performance and autonomously generate or update tools as needed. Responsibilities: Monitoring the performance and reliability of existing tools. Deciding when new tools or updates to current tools are necessary. Integrating new capabilities seamlessly into the system.

Examples Link to heading

Below are several concise examples that illustrate the general-purpose nature of this architecture, showcasing its ability to solve diverse tasks for various types of users:

Travel Planning: The agent identifies a need for a vacation plan, searches for airline and hotel APIs, auto-generates booking code, deploys the tool, and sends confirmation messages to the traveler.

Grocery Ordering: Recognizing a routine grocery run, the agent locates the relevant grocery API (e.g., Instacart), generates code to add items and complete checkout, and notifies the user via SMS or email.

Healthcare Scheduling: When a doctor’s appointment is needed, the agent finds the clinic’s scheduling API, creates code to book an appointment, and communicates the confirmed time and date to both patient and provider.

Financial Management: For a monthly budget review, the agent accesses bank APIs to retrieve transaction data, compiles a summary report, and emails the financial overview to the user.

Social Media Management: A business owner needs to schedule posts; the agent retrieves API documentation for platforms like Twitter or Facebook, auto-generates posting code, schedules content, and monitors engagement, sending periodic updates.

Event Coordination: When organizing a community event, the agent builds a tool that sends personalized invitations via email or SMS, tracks RSVPs, and sends follow-up reminders as needed.

Educational Assistance: A student looking to optimize their study schedule can rely on the agent to integrate with calendar APIs, generate reminders for classes or assignments, and send motivational messages based on past performance.

Each example demonstrates how the architecture’s core components—reasoning, search, code generation/deployment, communication, and self-improvement—work together to create adaptable, task-specific tools that meet a wide range of needs.