AI Agents Memory

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗠𝗲𝗺𝗼𝗿𝘆 is the most important piece of 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, this is how we define it 

In general, the memory for an agent is something that we provide via context in the prompt passed to LLM that helps the agent to better plan and react given past interactions or data not immediately available.

It is useful to group the memory into four types:

𝟭. 𝗘𝗽𝗶𝘀𝗼𝗱𝗶𝗰 - This type of memory contains past interactions and actions performed by the agent. After an action is taken, the application controlling the agent would store the action in some kind of persistent storage so that it can be retrieved later if needed. A good example would be using a vector Database to store semantic meaning of the interactions.

𝟮. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 - Any external information that is available to the agent and any knowledge the agent should have about itself. You can think of this as a context similar to one used in RAG applications. It can be internal knowledge only available to the agent or a grounding context to isolate part of the internet scale data for more accurate answers.

𝟯. 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗮𝗹 - This is systemic information like the structure of the System Prompt, available tools, guardrails etc. It will usually be stored in Git, Prompt and Tool Registries.

𝟰. Occasionally, the agent application would pull information from long-term memory and store it locally if it is needed for the task at hand.

𝟱. All of the information pulled together from the long-term or stored in local memory is called short-term or working memory. Compiling all of it into a prompt will produce the prompt to be passed to the LLM and it will provide further actions to be taken by the system.

We usually label 1. - 3. as Long-Term memory and 5. as Short-Term memory.

#LLM #AI #ContextEngineering

Google Genie 3 & where's it leading us to

1. Advancing “world models” for AI

AI agents need realistic, interactive environments to learn decision-making (e.g., how to navigate, manipulate objects, or plan actions).

Traditional simulators (like game engines) are hand-coded and slow to build. Genie 3 generates new, physics-aware environments instantly from text prompts.

This makes it useful for training AI at scale without needing human-designed levels.

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2. Democratizing content creation

Currently, building a game or simulation requires coding, asset design, and engines.

Genie 3 removes that barrier by letting anyone type a prompt (“a forest at sunset with floating islands”) and get an explorable world in seconds.

This could lead to personalized games, educational tools, or VR simulations without technical skills.

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3. Testing AI memory and reasoning

Genie 3 introduces visual memory (the AI remembers object placement for ~1 minute).

This allows researchers to study how AI handles continuity—a step toward agents that can remember and interact in more complex ways.

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4. Faster experimentation for researchers and developers

Instead of waiting weeks for artists and engineers to design levels, researchers can spin up thousands of unique worlds for experiments, robotics planning, or reinforcement learning.

Potential applications: autonomous driving, robotics training, creative prototyping.

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5. Laying groundwork for AI-generated entertainment

While not a finished product, Genie 3 hints at a future where games “write themselves” based on what you imagine.

Think: a Minecraft-like game that reshapes itself dynamically rather than relying on blocks or mods.

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In short: Genie 3 solves the problem of rapidly generating rich, interactive worlds without manual effort, which is crucial for AI development and creative prototyping, not just gaming

Framework for AI Workflow

Source

https://www.arxiv.org/pdf/2508.01186

Modern large language models (LLMs) are increasingly used as autonomous agents—capable of planning tasks, invoking tools, collaborating with other agents, and adapting to changing environments. However, as these systems grow more complex, ad hoc approaches to building and coordinating them are breaking down. Current challenges include:

1. Lack of standardized structures for how agents should coordinate, plan, and execute tasks.

2. Fragmentation of frameworks—academic and industrial systems vary widely in architecture, terminology, and capabilities, making comparison difficult.

3. Scalability and reliability issues—dynamic environments demand flexible workflows, but existing designs are often brittle or inefficient.

4. Security and trust concerns—multi-agent workflows introduce risks like tool poisoning, memory corruption, and collusion.

5. Absence of clear evaluation metrics—it’s unclear how to measure success or optimize these workflows systematically.

In other words, there’s no unified understanding of how to design, manage, and improve agent workflows. The paper proposes to address this by surveying current approaches, identifying their strengths and weaknesses, and outlining future research directions.

Train yourself in QA - Roadmap

Train yourself in QA

AI Industrial complex

The AI Industrial Complex is a critical term used to describe the growing network of companies, governments, research institutions, and military or security organizations that are driving the rapid development and deployment of artificial intelligence—often prioritizing power, profit, or control over ethical considerations.

It’s modeled on terms like the “Military-Industrial Complex,” which warned about entrenched systems where industries and governments reinforce each other’s interests, making oversight and reform difficult.

Core Features:

1. Concentration of Power

A few tech giants (e.g., OpenAI, Google, Anthropic, Microsoft) dominate AI research, infrastructure, and data access.

These companies influence policy and public narratives around AI risks and benefits.

2. State-Industry Alliances

Governments fund AI development for economic competition, surveillance, and defense.

In return, companies gain contracts, regulatory advantages, or subsidies.

3. Hype and Speculation

Fear of “falling behind” drives massive investment, often inflating promises of what AI can deliver.

Narratives about “AI safety” or “AI for good” can mask underlying motives (e.g., market control or militarization).

4. Ethical and Social Trade-offs

Labor displacement, surveillance, bias, and environmental costs are sidelined.

Smaller players and public interests struggle to influence the trajectory.

Why the term matters:

Critics use “AI Industrial Complex” to suggest that AI development isn’t purely about innovation but about consolidating power and shaping society around the interests of a few.

Do you want me to explain who coined it, examples of how it operates today, or why some argue it’s dangerous but others think it’s necessary?