Reinforcement Learning For AI Agents: Learning on the Job with Active Memory
VTAs I was saying in my previous post, for the last few years we have witnessed AI models evolve from powerful consultants into supervised partners, capable of tackling complex tasks like writing software. The clear next step in this evolution is towards autonomous agents that we can trust to work without direct supervision, iterating and improving on their own.
But there's a fundamental gap between today's agents and a truly autonomous future. A freshly trained model is like a brilliant graduate on their first day of work: full of knowledge but with little real-world experience. Unlike a graduate, models don't learn from their mistakes or successes on the job. To make the leap from a static tool to a dynamic partner, agents need to do what we do: learn from experience. The mathematical framework that we use to model learning from experience is called Reinforcement Learning.
Learning from Consequences: A Reinforcement Learning Primer
At its core, biological intelligence learns by interacting with its environment and observing the consequences of its actions. If an action leads to a good outcome, you're more likely to repeat it. If it leads to a bad one, you'll probably try something else next time.
In the world of AI, this concept is formalized as Reinforcement Learning (RL). The key components are:
- An agent (the learner) exists within an environment, and makes observations about it.
- The agent performs actions that may change the state of the environment.
- After each action, the agent receives a reward (or penalty): feedback that tells it what the utility of the action was.
- RL's approach is to develop a policy, which is essentially a strategy for the agent to maximize its cumulative reward over time.
- Often, this involves a trade-off between exploitation (using what has worked best in the past) and exploration (trying new things that might lead to even better rewards).
This continuous loop of observation-action-feedback is what allows an RL system to master everything from board games to robotic control.
The Problem: AI Agents with a Fixed Policy
Today's AI agents, particularly those built on large language models (LLMs), operate with a fixed policy. This policy is encoded in the billions of weights and parameters of the model, determined during its intensive training phase. Once that training is complete, the policy is effectively frozen.
This means the agent can't adapt to inputs that weren't represented in its training data. It will repeatedly make the same mistakes when faced with a similar problem, and it can't learn from the valuable feedback provided by its human users. The human user has to adapt to the agent's quirks, not the other way around. This is computationally expensive and inefficient, as solutions are re-derived from first principles every time.
To become truly useful, we need our agents to continue learning after they've been deployed.
Active Memory: A Framework for Runtime Reinforcement Learning
This is where the concept of an active, continuously improving, curated memory layer becomes transformative. It provides the mechanism for a live, reiforcement feedback loop that continuously refines an agent's effective policy by accumulating knowledge that is additive to that stored in the model weights.
Simple storage-and-retrieval, like in Retrieval-Augmented Generation (RAG), is not enough. RAG is passive; it memorises everything and then relies on retrieval to surface hopefully relevant content. An active memory approach, by contrast, is a dynamic process of curating and abstracting data into ever-evolving actionable knowledge. Memories are regularly consolidated, reinforcing those of high utility, and forgetting those that aren't helping. This is conceptually similar to performing a policy update in reinforcement learning.
Here's how an active memory framework maps to RL concepts:
Action: An AI agent proposes a solution to a problem (e.g., a block of code, a marketing email, or a step in a plan).
Reward Signal: The reward is accumulated from implicit and explicit feedback from the user's interaction with the agent. Did the user accept the proposed solution, or did they have to correct it? Was the solution successful? This outcome is the input to computing the reward signal.
Policy Update: This is a crucial step. Instead of just storing the interaction, our active memory curation process uses all feedback to update the knowledge in the memory, resulting effectively into an updated policy.
- Success (Positive Reward): A successful solution is remembered when observed for the first time, or has its value score adjusted in subsequent occurrences.
- Failure (Negative Reward): An unsuccessful or less than optimal approach is also remembered, and its value score adjusted, making it less likely to be proposed again.
- Consolidation: the memory regularly reorganises itself, to maintain accuracy and utility. Solutions may be decomposed into smaller, reusable components for future problems, generalised to higher levels of abstraction, or transformed in other ways. Over time, obsolete or consistently unhelpful memories are forgotten.
Through this process, the memory layer becomes a living repository of proven strategies. When a new problem arises, the agent doesn't just rely on the frozen weights in its base model. It also queries the living memory, exploiting the collective, curated experience of all previous interactions, effectively giving it a dynamically updating policy.
Example: Coding Agents Learning a Proprietary API
Imagine a coding agent tasked with using an internal, non-public API. This API was not part of the model's original training data, so the agent has no specific knowledge of the API's functions, patterns, or quirks.
First Attempt (Exploration): The agent relies on its general knowledge of programming. It makes an educated but incorrect guess about how to call the API. The code fails.
Human Feedback (Reward): A developer intervenes, corrects the code, and demonstrates the correct syntax and use of the API. This successful interaction is remembered, and associated with a positive reward signal.
Memory Curation (Policy Update): Our memory system ingests this entire episode. It doesn't simply save the final code; it links the initial problem to the successful solution. It abstracts any reusable patterns and stores them as high-value, actionable insights.
Future Attempts (Exploitation): The next time any agent in the organisation needs to use that API for a similar task, it queries the shared memory. The memory retrieves the known proven patterns and, with this additional knowledge, the agent now gets it right on the first try.
The agent has learned the "skill" of using this new API without costly retraining or fine-tuning of the foundation model. This experiential knowledge, learned by any one agent, is now instantly available to all agents and users, compounding the collective intelligence of the system.
Conclusion
Transforming AI agents into truly autonomous work partners requires us to solve the memory problem. We are moving beyond the shallow approach of simple storage and retrieval. By implementing an active memory system that can learn, generalise, evolve, and strategically forget, we are creating the framework for agents that learn on the job, from interactions with their environment, from their users, and from each other. This is how we move from clever but amnesiac helpers to dynamic, ever-improving collaborators.
At MemCo, we are building the active memory layer to unlock this next frontier in agentic AI. If you're interested in giving your agents a memory that learns, join the waitlist for Spark at memco.ai, or get in touch.
