Knowledge Base
Agentic AI Systems: Architectures, Frameworks, and Memory
Overview
Agentic AI represents a paradigm shift from reactive, prompt-driven models to autonomous systems that can plan, reason, and act to achieve complex goals. For machine learning practitioners, building these systems is a natural evolution that leverages foundational skills in prompt engineering, Retrieval-Augmented Generation (RAG), and Large Language Model (LLM) integration.
This reference document provides a technical overview of the core components required to design, build, and deploy production-ready agentic systems. It covers foundational architectural patterns, leading development frameworks, and advanced memory systems.
1. From Reactive Models to Agentic Systems
The key distinction between a standard LLM application and an agentic system is the presence of agency—the ability to operate autonomously in a loop of perception, planning, and action.
| Feature | Reactive System (Standard LLM / RAG) | Agentic System |
|---|---|---|
| Goal | Respond accurately to a single user prompt. | Achieve a multi-step, complex objective. |
| Planning | Minimal; follows the immediate instruction. | Decomposes a high-level goal into a sequence of actionable steps. |
| Action | Primarily text generation, sometimes with a single tool call (e.g., RAG retrieval). | Can select and execute a variety of tools (APIs, code interpreters, databases) dynamically. |
| Memory | Limited to the current conversation context window. | Employs both short-term (working) and long-term (persistent) memory to learn and adapt. |
| Autonomy | Low; requires explicit human input for each step. | High; operates in a continuous loop, making decisions and self-correcting without constant human guidance. |
2. Core Architectural Patterns
The behavior and reasoning of an AI agent are governed by its underlying architectural pattern. Choosing the right pattern is critical for balancing performance, cost, and complexity.
| Pattern | Description | Strengths | Weaknesses | Ideal Use Case |
|---|---|---|---|---|
| ReAct (Reason and Act) | The agent alternates between a “thought” step (reasoning about what to do next) and an “act” step (using a tool). This creates an iterative loop of thought -> action -> observation. | Simple to implement; effective for straightforward tasks where the next step is clear. | High token consumption and latency due to an LLM call at each step. Can get stuck in loops. | Simple chatbots, single-goal research tasks, basic tool use. |
| Plan-and-Execute | The agent first creates a complete, multi-step plan to achieve the goal. It then executes each step in sequence, often using smaller, more efficient models for the execution phase. | Lower cost and latency for complex workflows. More predictable and structured than ReAct. | The initial plan can be rigid and may fail if the environment changes unexpectedly. Requires a robust planning phase. | Multi-step data analysis, content generation pipelines, business process automation. |
| Reflexion (Reflection and Self-Correction) | An advanced pattern where the agent performs a task, then generates a critique of its own performance. It uses this linguistic feedback to refine its plan and retry the task, learning from its mistakes over multiple cycles. | Enables self-improvement and leads to higher accuracy on complex problems. Good for tasks with clear success criteria. | Slower and more computationally expensive. Requires a model capable of high-quality self-critique. | Complex code generation, mathematical problem solving, scientific research. |
3. The Tool/Action Layer: An Agent’s Capabilities
The Tool/Action Layer is the set of functions and capabilities an agent can execute to interact with the world. This includes everything from web search APIs and database connectors to code interpreters. The design of this library is a critical architectural consideration, as it defines the agent’s operational boundaries and is fundamental to its success.
For best practices on creating a robust and reliable toolset, refer to the guide on Designing Effective Agent Tools.
4. Key Frameworks for Agent Development
Several open-source frameworks have emerged to simplify the implementation of these architectural patterns, ranging from heavy programmatic libraries to lightweight text-based structures.
| Framework | Primary Focus | Key Features | Best For |
|---|---|---|---|
| LangGraph | Production-grade, stateful, multi-agent systems. | Built on LangChain; represents agents as nodes in a graph. Provides fine-grained control, state management (checkpoints), and excellent observability with LangSmith. | Building complex, reliable, and auditable agentic workflows for enterprise applications. |
| CrewAI | Rapid prototyping and role-based agent collaboration. | Intuitive, role-based abstraction (e.g., “Researcher,” “Writer”). Simplifies the creation of multi-agent teams that delegate tasks and work together. | Content creation pipelines, research teams, and scenarios where agents can be conceptualized as a “team of specialists.” |
| AutoGen (Microsoft) | Conversational multi-agent patterns and enterprise integration. | Flexible and extensible framework for complex agent collaboration. Now part of the unified Microsoft Agent Framework, with deep integration into Azure AI. | Research into complex agent interactions and building solutions within the Microsoft/Azure ecosystem. |
| Agentic Markdown (e.g., gstack) | Lightweight, text-based agent orchestration. | Uses structured Markdown files to define rigid operational roles (Product Manager, QA, DevOps) and constrain agent focus. | CLI-based agents (like Claude Code), rapid local development, and preventing LLM “mushy mode”. |
4.1 The Rise of Agentic Markdown
Beyond heavy programmatic frameworks, Agentic Markdown has emerged as a highly effective, lightweight alternative for structuring workflows. Because AI agents read Markdown natively, structured text has become a universal instruction set for generative AI [1]
Frameworks like Garry Tan’s gstack utilize a collection of plain Markdown files to operate as specific skills and operational boundaries for tools like Claude Code [1] By feeding the agent these highly structured files, system architects can force the model into rigidly defined personas—such as Product Manager, Engineering, QA, or DevOps. This approach prevents the agent from losing sight of the broader architectural context during complex tasks, ensuring that multi-step development workflows remain coherent and aligned with project goals [1]
Practitioner’s Note: It is recommended to master one framework at a time. Start with Agentic Markdown or CrewAI for rapid prototyping and role definition, then move to LangGraph for production-grade control and observability.
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5. Advanced Agent Concepts: Memory Systems
For an agent to perform long-running or context-dependent tasks, it requires a memory system that extends beyond the LLM’s limited context window.
5.1 Types of Agent Memory
| Memory Type | Description | Implementation Examples |
|---|---|---|
| Short-Term (Working) Memory | Stores information relevant to the current task or conversation session. It is typically volatile and resets after the task is complete. | In-memory dictionaries, Redis caches, LangGraph’s built-in state management. |
| Long-Term Memory | A persistent store of knowledge that the agent accumulates over time. This allows the agent to recall past interactions, learned facts, and user preferences across sessions. | Vector databases (for semantic search), knowledge graphs (for structured facts), traditional SQL/NoSQL databases. |
5.2 The Hybrid Memory Approach
The current best practice for production agents is a hybrid memory system that combines multiple strategies:
1. Semantic Retrieval: Use a vector store to retrieve relevant past experiences or documents based on the similarity to the current situation.
2. Factual Recall: Use a knowledge graph or a relational database to store and query structured information (e.g., user profiles, product specifications).
3. Summarization: Use an LLM to periodically summarize conversation history, compressing it to fit into the context window while retaining key information.
6. Practical Implementation and Evaluation
Building agentic systems requires a portfolio of practical skills that can be demonstrated through targeted projects.
| Project Type | Description | Core Skills Demonstrated |
|---|---|---|
| Autonomous Research Agent | An agent that takes a complex question, autonomously searches multiple web sources, synthesizes the findings, and generates a cited report. | Tool use (web search APIs), state management (tracking visited URLs), RAG, and planning. |
| Multi-Agent Content System | A “crew” of agents with specialized roles (e.g., Researcher, Writer, Editor, Fact-Checker) that collaborate to produce a polished article from a single topic prompt. | Multi-agent orchestration, task delegation, and role-based prompting. |
| Autonomous Data Analysis Agent | An agent that connects to a database or CSV file, understands natural language queries about the data, writes and executes code (SQL, Python) to find insights, and generates visualizations. | Code generation and execution, self-correction (debugging code), and advanced tool use. |
7. The Role of the Machine Learning Practitioner
Developing agentic systems is a natural extension of existing ML skills.
| Existing ML Skill | Application in Agentic Systems |
|---|---|
| Prompt Engineering | Designing the system prompts, instructions, and role definitions that guide the agent’s behavior and reasoning. |
| Retrieval-Augmented Generation (RAG) | Building the long-term memory systems that allow agents to retrieve and reason over private data. |
| Model Fine-Tuning | Fine-tuning smaller, specialized LLMs to act as efficient “worker” agents in a plan-and-execute or multi-agent system. |
| MLOps | Designing systems for observability (e.g., LangSmith), evaluation, and continuous deployment of agentic applications. |
8. Key Takeaways
- Agentic AI is a shift from reactive to proactive systems. Agents autonomously plan, act, and learn to achieve complex goals.
- Architectural patterns dictate agent behavior. ReAct, Plan-and-Execute, and Reflexion are the foundational patterns for building agents.
- Frameworks simplify development. LangGraph, CrewAI, and AutoGen provide robust tools for implementing these patterns in production.
- Memory is critical for advanced agents. A hybrid approach combining short-term state and long-term knowledge stores is the industry standard.
- Building agents leverages core ML skills. Prompt engineering, RAG, and MLOps are the building blocks for creating sophisticated agentic systems.
