Agentic AI Agents Go Mainstream in 2025 with Coherent Persistence

AI Agents in 2025: The Technical Reality Behind the Hype

AI Agents are really going mainstream in 2025, where 2023 was about chatbots and 2024 was about RAG.

The most interesting thing about AI Agents in 2025 isn't what they can do - it's how they're doing it. I've been digging into the technical architecture of modern AI Agents, and there are some fascinating patterns emerging that deserve more attention.

First, let's clarify what we mean by AI Agents. These aren't just LLMs responding to prompts anymore. Modern agents are stateful systems that maintain context across interactions, manage their own memory, and make autonomous decisions within defined boundaries. The key breakthrough has been in what I'm calling coherent persistence - the ability to maintain consistent behavior patterns across extended interactions.

The Modern AI Agent Technical Stack

Here's what's actually running under the hood of a typical modern agent:

class ModernAIAgent:
    def __init__(self):
        self.working_memory = CircularBuffer(max_size=1000)
        self.long_term_memory = VectorStore()
        self.action_space = ConstrainedActionSpace()
        self.context_manager = HierarchicalContext()

The interesting part is the HierarchicalContext. Unlike earlier agents that treated each interaction as a flat sequence, modern agents maintain multiple layers of context simultaneously. They're basically running a distributed state machine where different aspects of their behavior can evolve independently, where they are able to offload synchronous and asynchronous actions through AI tools or functions.

Multi-Agent Architectures

The real power comes from how these agents interact. I've been experimenting with networks of specialized agents, each handling different aspects of complex tasks. The breakthrough isn't in individual agent capabilities - it's in the protocols they use to coordinate.

Here's a typical agentic interaction flow:

The key insight: agents don't need to be general-purpose. They're most effective when they're specialized and coordinated through well-defined protocols. Different AI Agent frameworks vary in their implementation of the above diagram, and the results of their agents and predefined workflows may vary.

SQLite for AI?…Heck Yeah!

One pattern I'm particularly excited about is using SQLite as a persistent memory store for AI agents. It's lightweight, reliable, and - most importantly - human-readable. Here's a schema I've been using:

CREATE TABLE agent_memory (
    timestamp TEXT,
    context_hash TEXT,
    memory_type TEXT,
    embedding BLOB,
    content TEXT,
    metadata JSON
);

This makes agent behavior debuggable and allows for interesting queries across their memory space. Want to know how an agent's decision patterns have evolved? Just query the database! Of course, SQLite is not the only option for persistent memory storage. Based on your path from experimenting to prototype to production you may make other Agentic architectural design decisions.

The Observable Pattern

The most successful agent implementations I've seen follow what I'm calling the observable pattern - every action and decision is logged, queryable, and traceable. This isn't just about debugging; it's about understanding how agents actually operate in production. Imagine having 100s if not 1000s of agents in production; you will need a way to measure their performance and determine their predicability. That’s where evals are critical.

Below, is a simplified class for implementing agent decision trees:

@dataclass
class AgentDecision:
    timestamp: datetime
    context: dict
    options_considered: list
    chosen_action: str
    confidence: float

What's Next for AI Agents in 2025?

The most exciting developments aren't in making agents more powerful - they're in making them more predictable and maintainable. We're seeing the emergence of design patterns that make AI agents behave more like traditional software systems: testable, debuggable, and maintainable.

The future isn't in building bigger models or more complex architectures. It's in developing better patterns for composing and coordinating specialized agents. Think microservices, but for AI.

I’ll be publishing a series of articles presenting different implementations of AI Agents, along with other articles, over the next many months. Subscribe, keep an eye out and please let me know what you think!

---

Article cover image courtesy of Growtika on Unsplash