From: aidotengineer
Building production-ready AI agents efficiently requires a strategic approach to software development. A common trend among successful, widely-used AI agents is that they are not built on top of AI frameworks, but rather on AI primitives [01:13:00].
Why AI Primitives Over Frameworks?
AI frameworks are often criticized for being bloated, slow, and filled with abstractions that are not genuinely needed [01:21:00]. In contrast, AI primitives possess a native ability to perform exceptionally well in production environments [02:29:00].
A good analogy is Amazon S3, which is a low-level primitive for uploading and downloading data that scales massively without being a framework for object storage [02:37:00]. Similarly, in the rapidly evolving AI space, where new paradigms and LLMs emerge weekly [02:54:00], building on simple, composable building blocks is more advantageous [03:56:00]. This approach helps avoid being stuck with pre-built abstractions that may hinder future migration as LLMs improve [19:54:00].
The Challenge of Scaling AI Agents
Even with advancements in LLMs, building, deploying, and scaling AI agents remains a significant challenge [03:17:00]. AI agents represent a fundamentally new way of writing code, changing how coding projects and SaaS products are built [03:32:02]. For engineers transitioning into AI roles, simplifying the process of building production-ready AI agents is crucial [04:45:00].
Starting with frameworks often leads to difficulties in debugging obscure abstractions and then figuring out how to deploy and scale those agents [04:58:00]. Instead, building on predefined, highly scalable AI primitives is recommended [05:15:00].
Essential AI Primitives for Scalable Agents
Key AI primitives that support scaling AI solutions in production and AI agents include:
- Memory: An autonomous engine that acts as a vector store to manage and search large volumes of data (terabytes), enabling automatic scaling for agents [05:24:06].
- Threads: Used to store and manage context or conversation history for an agent, crucial for maintaining continuity [04:01:06].
- Parser: Extracts context from various file types, such as converting PDF to text [08:14:00].
- Chunker: Splits extracted content into smaller, manageable pieces for efficient similarity search [08:20:00].
- Tools Infrastructure: Allows agents to automatically call external APIs or services, extending their capabilities [11:33:00].
- Workflow Engine: Specifically designed for multi-step agent processes [10:30:00].
When AI agents are constructed with these composable primitives, especially those with integrated cloud capabilities, they inherently become serverless and can automatically handle heavy lifting [05:42:00].
Common AI Agent Architectures Leveraging Primitives
Using AI primitives enables the construction of diverse and complex agent architectures:
- Augmented LLM: An agent that combines an LLM with access to tools, threads, and memory to generate output based on input [11:18:00].
- Prompt Chaining and Composition: Multiple agents work sequentially, where the output of one agent informs the next, such as an email spam detector followed by a response generator [13:06:00]. This is typically implemented with plain JavaScript/TypeScript [13:39:00].
- Agent Router / LLM Router: An agent or LLM acts as a decision-maker, determining which specialized agent (e.g., summary, reasoning, coding) should be invoked next based on the input [14:00:00].
- Running Agents in Parallel: Simple JavaScript constructs like
Promise.allcan execute multiple agents (e.g., sentiment analysis, summarization, decision-making) concurrently [16:47:00]. - Agent Orchestrator-Worker: An orchestrator agent plans and creates subtasks, which are then distributed to worker agents. The results from worker agents are then synthesized by another agent, mimicking deep research agent architectures [17:10:00]. This pattern, despite its complexity, can be implemented with minimal lines of code (e.g., 90 lines) using primitives [19:08:00].
- Evaluator-Optimizer: An agent generates content (e.g., marketing copy), which is then evaluated by another LLM acting as a “judge.” This judge provides feedback for iterative improvement until the desired quality is met [20:08:00].
- Memory-based Agents: Involves uploading data to a memory primitive, and then using an agent to retrieve and answer questions based on that data [21:50:00].
Conclusion
By understanding and utilizing these AI primitive patterns, developers can construct approximately 80% of the most complex AI agents currently in existence [22:24:00]. This approach fosters flexibility and adaptability in a rapidly changing field, allowing developers to build sophisticated agents like deep researchers or receipt checkers by composing simple, powerful primitives [22:34:00]. The focus remains on building with efficient, scalable building blocks rather than rigid, overly abstracted frameworks [25:28:00].