The Evolution of AI Agents: Architectures, Durability, and Caching

AI agents have evolved remarkably since the early days of AI. The agent was long used to describe early NLP efforts to create an intelligent AI system. Then, in the 2010s, agents became more commonly associated with reinforcement learning as self-learning agents.. Common applications were agents playing video games in the early days of OpenAI or some stock trading agents. Since the inflection point of LLMs agents have come to mean something a bit different. Now representing fully autonomous systems orchestrating complex workflows at massive scale. At Bytewax, we’re always thinking about how streaming data, durable state, and cost efficiency intersect in this new wave of AI-driven software. In this post, we’ll explore what an AI agent is in 2025, how leading-edge architectures tackle durability, and why caching can make or break your bottom line.

What Is an AI Agent in 2025?

The term “AI agent” has been tossed around in everything from game development to enterprise automation. But in 2025, the definition has sharpened into a few core characteristics:

1. Autonomy: Modern AI agents can operate with minimal human intervention. While they still need initial instructions or constraints, their ability to make decisions without constant supervision is paramount.

2. Contextual Awareness: Agents don’t just react; they continuously evaluate the data streams around them—user inputs, sensor data, or external APIs—and adapt their next steps accordingly.

3. Goal Orientation: Instead of rote scripted actions, agents optimize toward certain goals (e.g., maximizing user satisfaction, reducing latency, or staying within budget constraints).

4. Scalable Collaboration: Agents often work in concert. They delegate tasks to specialized sub-agents or external tools, weaving together a tapestry of micro-decisions that drive business-critical outcomes.

If you’re building or adopting an AI agent in 2025, you’re no longer just spinning up a single microservice that spits out predictions; you’re deploying a system that can live, learn, adapt, and coordinate with other services in real time.

Likely AI Agent Architectures in 2025

As the AI ecosystem has matured, several architectural patterns have come to the forefront for agents:

1. Actor-Based Architectures

Borrowed from distributed computing and streaming frameworks, the actor model maps nicely to agent design. Each agent (or sub-agent) is treated as an actor with its own message queue and internal state. Popular frameworks like Ray and Akka help orchestrate and scale these actor-based systems seamlessly. The actor model ensures high concurrency and fault tolerance—ideal attributes when running a fleet of autonomous AI agents.

2. Layered Orchestrator + Specialized Sub-Agents

A trending approach is to have an “orchestrator agent” that focuses on high-level decisions and delegates specialized tasks to sub-agents. The orchestrator often leverages large language models (LLMs) for reasoning, while sub-agents might be simpler but more efficient tools for niche tasks (e.g., vector database lookups, complex data transformations, or image recognition). Architectures like LangChain and other prompt orchestration frameworks have introduced robust ways to define how these sub-agents interact and pass context between themselves.

3. Streaming Data Processing + Real-Time Feedback

Real-time AI agents must handle streaming data at scale. Systems like Bytewax (for building stateful data pipelines in Python), Flink, or Spark Structured Streaming can provide the continuous ingestion and transformation of data that feeds into an agent’s decision-making process. As the agent receives and processes new events, it refines its understanding of the environment, leading to more accurate and timely actions.

Durable Runtimes and State Mechanisms

One challenge with long-running agents is how to maintain and manage state. Because an agent isn’t just a single inference call—it’s a living process—you need a robust mechanism to handle context, memory, and logs over time. This is especially true for:

• Persistent Memories: Storing past conversations, decisions, or user preferences so that the agent can refer back to them in future interactions.
• Recovery: If an agent’s runtime restarts due to a failure or version update, it should pick up right where it left off without losing crucial information or repeating the same tasks.

To achieve this, forward-looking AI teams rely on durable runtimes and stateful data-processing frameworks. Tools like Bytewax bring robust state management directly into your Python code, letting you checkpoint agent state frequently so nothing is lost if things go sideways. At scale, you might combine this with distributed key-value stores (like Redis or DynamoDB) or specialized event sourcing systems to ensure that an agent’s entire “life history” is consistently backed up.

Why Caching Mechanisms Are Critical

Unlike a simple microservice returning a single inference, AI agents often run many inference calls, chain them with context, and produce iterative refinements. That means costs (especially from large language models) can quickly balloon if every inference is done from scratch.

Caching mechanisms come to the rescue here:

1. Response Caching: If the agent sees similar requests or replays a decision path, an LLM-based tool can reuse recent outputs. This significantly cuts down on API usage, saving time and money.

2. Embedding Caching: Many architectures rely on vector embeddings for semantic search or contextual understanding. Storing these embeddings in a local or distributed cache means you only compute them once per unique data item.

3. Workflow-Level Caching: Large AI workflows sometimes re-run partial tasks. In agent-based systems, caching sub-results across multiple runs avoids redoing expensive computations, speeding up decisions and cutting cloud spend.

In 2025, a well-tuned caching layer can be the difference between paying thousands of dollars versus millions for compute and model usage. Even a straightforward key-value store (like Redis) or an in-memory solution embedded in your streaming engine can yield outsized benefits if integrated with care.

Putting It All Together

AI agents in 2025 aren’t just about building a chatbot or hooking up a neural network to your CI/CD pipeline. They’re autonomous systems—sometimes spanning dozens or even hundreds of actors—coordinating tasks in real time and persisting their state across restarts, redeployments, and data surges. Achieving this level of resilience demands a careful blend of:

• Durable Runtimes: So you never lose context or replay expensive workflows unnecessarily.
• Strategic State Storage: State is your agent’s memory bank. It’s critical for personalization, reasoning, and accountability.
• Caching at Every Layer: From embeddings to entire responses, caching lowers latency and manages cost.

By weaving these elements into your AI agent architecture—together with a reliable streaming foundation—you’ll be poised to tackle the expanding frontier of AI-driven applications. And if you’re looking to offload the complexities of stateful streaming in Python, be sure to explore how Bytewax’s open-source framework fits into your toolkit for building real-time, AI-enhanced data pipelines.

Next Steps

• Check out Bytewax: If you’re ready to build real-time stateful pipelines for your AI agents, take a look at Bytewax on GitHub.
• Evaluate Your Caching Strategy: Map out your current or planned agent architecture and see where you might benefit from caching—both for performance and for cost optimization.
• Stay Tuned: The agent landscape is evolving rapidly. We’ll continue to share best practices and integration patterns as the community around AI agents matures.

We’d love to hear how you’re thinking about AI agents in 2025. Drop us a note, or open an issue on our GitHub repo!