Agents That Learn: The Missing Layer in AI Systems

There is a gap between what we expect from AI agents and what they actually deliver. We expect them to get better over time. To remember us. To learn from experience. Instead, every conversation starts from zero.

This is not a bug in any particular framework. It is how Large Language Models fundamentally work. The model does not remember the messages it received, the tool calls it made, or what happened three turns ago. If you want an agent that learns, you have to build that capability yourself.

Memory Is a Noun. Learning Is a Verb.

This distinction matters more than it appears.

Memory is static: a database of facts. You store information, you retrieve information. The system does not change.

Learning is dynamic: it evolves, compounds, gets sharper. Memory stores what you said. Learning figures out what it means.

When Ashpreet Bedi, founder of Agno, articulated this distinction recently, he identified something that the industry has been conflating. Most “memory” solutions for agents are really just persistent storage with retrieval. They make agents remember. They do not make agents learn.

What Learning Is Not

Before we define what runtime learning looks like, let’s clear up what does not count.

Session history is not learning. It is a transcript that gets thrown away when the session ends. Useful for context within a conversation. Useless across conversations.

RAG is not learning. RAG is retrieval. You loaded static documents. The agent can search them, but it did not discover anything. It is not getting smarter. The knowledge base today is the same knowledge base tomorrow.

Fine-tuning is not learning. Fine-tuning happens offline. Your agent cannot learn while it is running. And you probably do not want to fine-tune on every conversation anyway. The feedback loop is too slow.

What Runtime Learning Looks Like

An agent that truly learns:

Remembers users across sessions. Name, role, preferences, working style—captured automatically and recalled when relevant.
Captures insights from conversations. Not everything is worth saving. The agent develops judgment about what matters.
Learns from its own decisions. Why did it recommend Python over JavaScript? Why did it search the web instead of answering from memory? Decision logs capture reasoning for auditing and improvement.
Transfers knowledge across users. This is the breakthrough. When one person teaches the agent something, another person benefits from it.

Three Levels of Learning

The Agno framework, which implements this architecture, organizes learning into progressive levels:

Level 1: The Agent Remembers You

The simplest form of learning captures user profiles and memories automatically. After each interaction, the system extracts:

User Profile: Structured facts—name, role, company, preferences. These get updated in place as new information arrives.

User Memory: Unstructured observations—“prefers concise responses,” “works on ML projects,” “mentioned struggling with async code.” These accumulate over time.

No explicit tool calls. No manual context injection. The agent just learns who you are and adapts.

Level 2: The Agent Captures Insights

For some types of learning, you want the agent to decide what is worth saving. Not everything in a conversation is valuable. The agent should have judgment.

In Agentic mode, the agent receives tools: save_learning, search_learnings. It decides when to use them.

When a user shares something genuinely useful—a non-obvious insight, a best practice, a pattern that might help others—the agent saves it. When answering a question, the agent searches for relevant prior learnings first.

The agent also logs its decisions with reasoning. When something goes wrong, you know why.

Level 3: Knowledge Compounds Across Users

This is where things get interesting.

Session 1, Engineer 1: “We are trying to reduce our cloud egress costs. Remember this.”

The agent saves the insight.

Session 2, Engineer 2 (different user, different session, a week later): “I am picking a cloud provider for a data pipeline. Key considerations?”

The agent surfaces the egress cost insight. Unprompted. No shared context. No explicit handoff. Engineer 2 benefits from what Engineer 1 discovered.

One person taught the agent something. Another person benefited from it.

No fine-tuning. No RLHF infrastructure. Just a database, a vector store, and some prompt engineering. The team at Agno calls this “GPU Poor Learning.” It works.

The Architecture

Learning happens through learning stores. Each store captures a different type of knowledge:

Store	Scope	Purpose
User Profile	Per user	Name, role, preferences
User Memory	Per user	Observations from conversations
Session Context	Per session	Goals, plans, progress
Entity Memory	Configurable	Facts about companies, projects, people
Learned Knowledge	Global	Insights that transfer across users
Decision Log	Per agent	Decisions with reasoning

Each store can operate in a different learning mode:

Always: Extraction runs automatically after each response
Agentic: Agent receives tools and decides what to save
Propose: Agent proposes learnings, human confirms before saving

Mix and match. Automatic profile extraction. Agent-driven knowledge capture. Human-approved insights for high-stakes domains.

Why This Matters for Enterprise

Claude’s memory feels magical. It is natural, contextual, never announces “saving to memory.” It just knows you.

But you cannot build with it. Claude’s memory is a consumer product feature. The API gives you nothing. If you want learning for your agents in production, you are on your own.

This creates a strategic gap. Consumer AI products get smarter with every interaction. Enterprise AI deployments stay static.

The implications:

Support agents that get better with every ticket. Ticket #1000 gets resolved faster because the agent learned from tickets #1-999. Solutions that worked. Patterns that recur. Gotchas to avoid.

Coding assistants that learn your codebase. Not just RAG over your docs—actual learning. How you test. How you structure code. What your team’s conventions are. The agent adapts to your way of working.

Team knowledge that compounds. When one analyst discovers something, the whole team benefits. No Slack message that gets buried. No wiki page that gets stale. The knowledge lives in the agent.

The agent on day 1000 is fundamentally better than it was on day 1.

Governance Considerations

Runtime learning introduces new governance challenges.

Data sovereignty: Everything runs in your infrastructure. Your database. Your vector store. Your cloud. No data leaves your environment. You own the knowledge.

Auditability: Decision logs create an audit trail. Why did the agent make that recommendation? What prior learnings influenced it? You can trace the reasoning.

Knowledge quality: What happens when the agent learns something wrong? Agentic mode gives you control—propose learnings, review before saving, delete incorrect entries.

Cross-user privacy: If knowledge transfers across users, who can access what? Role-based filters, topic boundaries, and sensitivity classification become essential.

These are solvable problems. But they must be solved deliberately.

The Learning Protocol

For teams building custom learning capabilities, Agno defines a four-method protocol:

class MyCustomStore(LearningStore):
    def recall(self, **context) -> Optional[Any]      # Get data
    def process(self, messages, **context) -> None    # Extract & save
    def build_context(self, data) -> str              # Format for prompt
    def get_tools(self, **context) -> List[Callable]  # Give agent tools

Four methods. Approximately 50 lines. Your domain, your rules.

Legal documents. Medical records. Codebases. Sales pipelines. Whatever knowledge your agents need to accumulate.

Practical Implications

If you are building agents today, consider:

Start with user profiles. The simplest form of learning delivers immediate value. Agents that remember preferences feel fundamentally different to use.
Add decision logging early. Even if you do not act on it initially, the audit trail is valuable. When something goes wrong, you will want to know why.
Be deliberate about cross-user knowledge. The compounding effect is powerful but introduces privacy and quality considerations. Start with explicit save commands before enabling automatic extraction.
Own your data. Use self-hosted solutions. Your learnings are a competitive asset. They should not live in someone else’s cloud.
Build feedback loops. Learning without feedback is accumulation without improvement. Track which learnings get used and which get ignored.

The Bottom Line

Most agents are stateless. They reason, respond, forget. Every conversation starts from zero.

This is a choice, not a constraint. The technology to build learning agents exists today. It does not require fine-tuning or RLHF or massive infrastructure. A database, a vector store, and thoughtful engineering.

The question is whether your agents will be tools that never improve or teammates that get better with every interaction.

At Victorino Group, we implement governed agentic AI with persistent learning capabilities. Agents that remember, learn, and improve—while maintaining full control over data and decisions. If that is what you need, let’s talk.

Sources: This analysis draws on Ashpreet Bedi’s “Build Agents That Learn” (January 2026), the Agno framework documentation, and our implementation experience with enterprise learning agents.