Level 1: Code

Explain cognitive architectures in simple terms: blueprints for the mind of an AI. Key components (drawing from the LangChain article, e.g., memory, perception, reasoning, learning, decision-making/action).

Recently, we

Code

LLM Calls/Chains

sequence of LLM calls deterministic, eason to reason about

Router

LLM decide which next steps to run: which tools? which retries?

This is your DAG you might know from different orchestration tools (no cycles)

State Machine

  • with cycles, but sequence of steps are deterministic
  • what are the possible next steps are usually clear

Agent

  • decides to use a tool that wasn’t there in the first place
  • flexible but less reliable

Blog Post Structure: Cognitive Architectures, RAG, and the Power of Agents

Chosen Title: Cognitive Architectures in Action: Structuring RAG and Agentic Systems Overall Teaser for Social Media / Intro Snippet: “Is AI just a black box, or can we build truly ’thinking’ machines? We dive into cognitive architectures, trace the journey of RAG, and explore how new ‘Agentic RAG’ is a game-changer – sometimes with surprisingly simple tools. Inspired by insights from LangChain and recent dev chatter. #AI #CognitiveArchitecture #RAG #AgenticAI #TechBlog” I. The Building Blocks of AI: Exploring Cognitive Architectural Patterns

  • Content:

    • What are they? Explain cognitive architectures in simple terms: they are high-level blueprints or design patterns for the mind of an AI, outlining how different cognitive functions interact.

    • Core Idea: Instead of one monolithic model, they often involve multiple components working in concert.

    • Key High-Level Components (General Functions): Briefly touch upon the foundational elements often found in these architectures (drawing from the LangChain article’s spirit, e.g., memory, perception/sensing, reasoning, learning, planning, decision-making/action). These are the functions the patterns below help implement.

    • Visual (as requested): ![](https://blog.langchain.dev/content/images/size/w1000/2024/07/Screenshot-2024-06-28-at-7.33.10-PM.png) *(Caption idea: A visual representation of components in a cognitive architecture, courtesy of LangChain.)*

    • Common Architectural Patterns/Paradigms:

      • A. Code:
        • The most fundamental level. Traditional software logic.
        • Deterministic, explicit instructions.
        • Often forms the backbone or integrates other components.
        • Example: A hard-coded rule to always query a specific database if a certain keyword appears.
      • B. LLM Calls/Chains:
        • Sequences of LLM calls, where the output of one LLM (or a processing step) feeds into the next.
        • Generally deterministic in their flow (though LLM output itself has variability).
        • Relatively easy to reason about the sequence of operations.
        • Example: Summarize a document (LLM call 1), then extract keywords from the summary (LLM call 2).
      • C. Router:
        • An LLM (or other logic) decides which path or next step(s) to take from a set of predefined options.
        • Can select which tools to use, which knowledge sources to query, or which sub-tasks to execute.
        • Often forms a Directed Acyclic Graph (DAG) – a flow of decisions without (typically) loops back to the same router immediately.
        • Example: Based on user query, route to a “product_info_retriever” or a “customer_support_faq_retriever”.
      • D. State Machine:
        • A system that can be in one of a finite number of states. Transitions between states are triggered by inputs or conditions.
        • The sequence of steps can be deterministic given the current state and input.
        • Allows for cycles (e.g., retry loops, conversational turns that return to a similar state).
        • Possible next steps are usually clearly defined from any given state.
        • Example: A chatbot managing an order: [CheckOrderStatus] -> [ProvideUpdate] -> [AskIfFurtherHelpNeeded] -> [EndInteraction] or back to [CheckOrderStatus] for another item.
      • E. Agent:
        • The most sophisticated and autonomous pattern.
        • An agent uses an LLM (or other reasoning engine) to decide on a sequence of actions to take.
        • Crucially, it can choose to use tools (APIs, functions, other chains, retrievers) that might not have been explicitly pre-determined for every single step. It has a repertoire of tools and decides when and how to use them.
        • Can plan, reflect on results, and adapt its strategy.
        • Flexible and powerful, but can be less reliable or predictable than simpler patterns due to the expanded decision space.
        • Example: A research agent given a complex question might decide to first search the web, then query a database, then summarize findings, and if information is missing, decide to try a different search strategy or ask a clarifying question.
  • Teaser for this section: “Let’s look under the hood: We’ll examine the core design patterns like code, LLM chains, routers, state machines, and agents that define how AI systems perceive, reason, and act within a cognitive architecture.”

II. RAG: Grounding LLMs in Reality

  • Content:
    • The Core Problem RAG Solves: Briefly recap the limitations of standalone LLMs (knowledge cutoffs, potential for hallucination, lack of access to private/real-time data).
    • RAG to the Rescue: Introduce Retrieval Augmented Generation as the fundamental technique to connect LLMs to external knowledge sources.
    • Basic RAG Workflow: The classic two-step: 1. Retrieve relevant information from a knowledge base. 2. Generate a response using the LLM, augmented by this retrieved context.
  • Teaser: “LLMs are brilliant, but how do we keep them factual and current? A quick primer on RAG – the essential technique for giving your AI a library card to the world’s (or your company’s) information.”

III. RAG Applied to Cognitive Architectures: An Evolutionary Tale

  • Content:
    • Basic RAG as a “Chain” or “Code”:
      • The simplest RAG implementations can be viewed as a fixed sequence: [Retrieve Docs] -> [Stuff into Prompt] -> [Generate Answer]. This aligns well with the “LLM Calls/Chains” pattern or even “Code” if the retrieval is very programmatic.
    • RAG with a “Router”: Introducing Choice:
      • Slightly more advanced RAG might use a “Router” to decide which knowledge base to query or which retrieval strategy to employ based on the input.
      • Example: A router deciding whether to use dense vector search for semantic queries vs. keyword search for specific term lookups.
    • Agentic RAG: Leveraging “State Machines” and “Agents”:
      • This is where RAG truly becomes dynamic and “intelligent.”
      • State Machines for Iterative RAG: An agent might operate as a state machine, performing retrieval, generating, then evaluating if more information is needed (state change), and potentially re-querying or refining (cycle).
      • The “Agent” Pattern as the Pinnacle of RAG:
        • An Agentic RAG system embodies the “Agent” architecture. It has a goal (answer the user’s query accurately and comprehensively).
        • It can autonomously decide to use various retrieval tools (vector stores, keyword search, SQL databases, web searches, APIs).
        • It can plan multi-step retrieval strategies (e.g., “First, I’ll search document X, then based on that, I’ll query API Y”).
        • It can reflect on the quality of retrieved information and decide to try different approaches if the initial results are poor.
        • This is where RAG moves from a passive information provider to an active information seeker and synthesizer, deeply integrating with reasoning and decision-making components of a cognitive architecture.
  • Teaser: “RAG isn’t one-size-fits-all. See how it maps from simple, fixed chains to sophisticated, decision-making agents within the landscape of cognitive architectures. This is RAG growing up!”

IV. The Pragmatic Powerhouse: Agentic RAG + Basic Keyword Search?

  • Content:
    • Introduce the observation from recent tweets/developer discussions: Agentic RAG frameworks orchestrating simpler retrieval methods (like basic keyword search, or specific database lookups) are showing significant value, especially for low-to-medium complexity tasks.
    • Why this is powerful & makes sense:
      • The Agent is the Key: The intelligence lies in the agent’s ability to choose the right tool for the job. If a keyword search is sufficient and efficient for a sub-task, the agent can opt for that.
      • Lower Complexity & Cost for Certain Retrievals: Keyword search is often computationally cheaper, faster to set up, and easier to debug than maintaining large vector embeddings for all types of data.
      • Precision for Known Terms: For exact matches or known identifiers, keyword search can be more reliable than semantic similarity.
      • Hybrid Approaches: An agent can decide to use keyword search first, then fall back to vector search if needed, or combine results.
    • Implications:
      • Focus shifts to the quality of the agent’s reasoning and tool-selection capabilities.
      • Democratizes access to powerful agentic patterns without always needing the most complex/expensive retrieval backend for every step.
      • Highlights the value of a diverse toolkit for the agent.
  • Teaser: “Does Agentic RAG always need a PhD in vector calculus? Not necessarily! Discover why savvy agents using good old keyword search are becoming unsung heroes, delivering impressive results with surprising simplicity.”

V. Conclusion: Building More Capable, Understandable, and Practical AI

  • Content:
    • Recap: Cognitive architectures give us a way to think about designing complex AI. RAG started as a way to ground LLMs and has evolved into sophisticated Agentic RAG, which embodies advanced cognitive patterns.
    • The key insight: The trend is towards AI that can reason, plan, and use tools flexibly. This doesn’t always mean maximum complexity at every layer; intelligent orchestration of simpler components can be highly effective.
    • The future is likely hybrid: sophisticated agents using a mix of advanced and foundational tools, guided by well-designed cognitive architectures.
    • Call to action: Encourage readers to think about their own AI projects in terms of these architectural patterns and to share their experiences or thoughts on the “simple but effective” agentic approaches.
  • Teaser/Final Thought: “As we build the next generation of AI, it’s not just about bigger models, but smarter designs. Cognitive architectures and adaptable agents (even those using simple tools effectively!) are lighting the path forward. What’s your blueprint for intelligent AI?”

This structure feels robust and directly addresses your input. It builds a clear narrative from foundational concepts to cutting-edge applications and practical insights.