{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreihu5zb4k2rejinjx2n3tvqxra74in627yzmf35x5tu4acijyrkzxu",
    "uri": "at://did:plc:25rdn5elo5izoxrmtis34zuk/app.bsky.feed.post/3mokjtus7tpa2"
  },
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreigjtqt3bkziz3o32lblova34pexbln4hn4h3suexzqxjbgwwd36xe"
    },
    "mimeType": "image/webp",
    "size": 253442
  },
  "path": "/ponmiledaniel/understanding-coala-a-cognitive-architecture-for-language-agents-2023-33mh",
  "publishedAt": "2026-06-18T09:27:12.000Z",
  "site": "https://dev.to",
  "tags": [
    "ai",
    "programming",
    "computerscience",
    "agents",
    "https://arxiv.org/abs/2309.02427"
  ],
  "textContent": "**Note:** This article is a summary and interpretation of the research paper\n\n> Cognitive Architectures for Language Agents\n>  (2023) by Michael Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L. Griffiths. Rather than proposing a new architecture, the goal here is to explain the paper's core ideas in an accessible way and explore why they matter for the future of AI memory systems.\n\nModern language agents feel intelligent, but under the hood they are still fragile systems stitched together with prompts, context windows, and external tools. The CoALA framework (Cognitive Architectures for Language Agents) proposes a more structured view: instead of treating LLMs as standalone reasoners, we should treat them as components inside a cognitive system with memory, actions, and decision loops.\n\nAt the center of this framework is a simple idea borrowed from cognitive science: intelligence depends heavily on how memory is structured.\n\nCoALA breaks memory into four interacting systems: **working memory, episodic memory, semantic memory, and procedural memory**.\n\n**Working Memory: The Active Scratchpad**\n\nWorking memory is the agent’s current “mental workspace.”\n\nIt contains everything the model is actively using right now:\n\n  * the current prompt or conversation\n  * intermediate reasoning steps\n  * tool outputs\n  * short-lived goals and constraints\n\n\n\nIn CoALA terms, this is the active state used for the current decision cycle. It is not persistent. Once the cycle ends, it disappears unless explicitly stored elsewhere.\n\nYou can think of it as the agent’s RAM fast, limited, and constantly overwritten.\n\nMost LLM systems today stop here. That is why they feel intelligent but inconsistent: they reason well in the moment, but forget the structure that produced good decisions.\n\n**Episodic Memory: What Happened Before**\n\nEpisodic memory stores experiences over time.\n\nInstead of storing abstract facts, it stores:\n\n  * past interactions\n  * task trajectories\n  * failures and corrections\n  * sequences of actions taken in specific contexts\n\n\n\n**Example:**\n\n> The agent tried to deploy a service, forgot database migrations, caused a rollback, then fixed it by adding a pre-deploy migration check.\n\nThis is important because intelligence is not just knowing facts it is learning from sequences of events.\n\nIn CoALA, episodic memory supports learning from history, enabling agents to improve across repeated tasks instead of resetting every session.\n\nWithout episodic memory, agents repeat mistakes endlessly.\n\n**Semantic Memory: What the Agent Knows**\n\nSemantic memory is the system’s store of general knowledge.\n\nThis includes:\n\n  * facts about the world\n  * domain knowledge\n  * definitions and relationships\n  * stable abstractions\n\n\n\nExample:\n\n> PostgreSQL requires schema migrations before deployment in production systems.\n\nUnlike episodic memory, semantic memory is not tied to a specific experience. It is decontextualized knowledge that can be reused across situations.\n\nIn modern systems, this is often implemented through:\n\n  * vector databases\n  * retrieval-augmented generation (RAG)\n  * knowledge graphs\n\n\n\nSemantic memory is what allows agents to “know things” even if they have never directly experienced them.Procedural Memory: How to Do Things\n\n**Procedural memory stores skills and action patterns.**\n\nThis is not about facts or past events. It is about behavioral structure:\n\n  * step-by-step workflows\n  * tool usage patterns\n  * decision heuristics\n  * learned strategies\n\n\n\n**Example:**\n\n> To deploy safely: run tests → build → apply migrations → deploy → verify health checks.\n\nThis type of memory is critical because it encodes execution ability, not just knowledge.\n\nIn CoALA, procedural memory can live in:\n\n  * explicit rules\n  * agent code\n  * learned policies\n  * tool-use patterns embedded in prompts or fine-tuning\n\n\n\nWithout procedural memory, an agent may “know” what to do but fail to consistently execute it.\n\n**Why These Four Matter Together**\n\nThe key insight in CoALA is that intelligence emerges from coordination between memory types, not from scaling a single context window.\n\n  * Working memory handles the present\n  * Episodic memory improves learning from experience\n  * Semantic memory provides knowledge grounding\n  * Procedural memory encodes behavior and execution\n\n\n\nMost current LLM systems collapse all of this into one space: the prompt. CoALA argues that this is fundamentally limiting.\n\nInstead, memory should be treated as a structured system with different persistence rules, update mechanisms, and retrieval strategies.\n\n**Closing Thought**\n\nCoALA reframes language agents not as chat models, but as cognitive systems with modular memory and structured decision loops. It connects modern LLM tooling back to decades of cognitive science, showing that we are rebuilding ideas that were already explored in symbolic AI just now with language models as the core engine.\n\nThis raises an interesting question: **if AI can organize memories like humans, can memory do more than just retrie**\n\n**Reference:**\n\nSumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. Cognitive Architectures for Language Agents. arXiv, 2023. https://arxiv.org/abs/2309.02427",
  "title": "Understanding CoALA: A Cognitive Architecture for Language Agents (2023)"
}