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Benefits of AI primitives in production

4 min read

From: aidotengineer
The Benefits of AI Primitives in Production

AI primitives offer a superior approach to building and scaling AI agents in production compared to traditional AI frameworks [00:01:01]. Production-ready AI agents, such as those found in Perplexity, Cursor, and Chai, consistently demonstrate a common theme: they are not built on top of AI frameworks [00:00:58].

Why Primitives Over Frameworks?

Frameworks are often criticized for being bloated, slow-moving, and filled with unnecessary abstractions [00:01:21]. They introduce obscure abstractions that are difficult to debug, complicating the process of deploying and scaling AI agents [00:05:01]. In a rapidly evolving field where new paradigms and LLMs emerge frequently, frameworks can become restrictive [00:25:54]. Building with AI primitives allows for greater flexibility and adaptability, making it easier to integrate new advancements without being constrained by a pre-built abstraction [00:19:56].

Instead, AI primitives provide small, composable building blocks that are natively well-suited for production environments [00:02:26]. An analogy is Amazon S3, a simple, low-level primitive for object storage that scales massively without needing a framework [00:02:32]. This approach aims to provide the fastest possible way to build a production-ready AI agent [00:04:50].

Key AI Primitives and Their Benefits

Commonly used AI primitives include:

  • Memory (e.g., langbase.memories): An autonomous long-term storage engine that can incorporate vector stores, handling terabytes of data and scaling automatically [00:05:24] [00:10:28].
  • Threads: Used to store and manage context and conversation history for an agent [00:04:06] [00:10:35]. This allows for asynchronous context management and scratchpad functionality, similar to retaining information when booking a flight [00:11:52].
  • Parser: Extracts context from various file types, converting them into a usable format (e.g., PDF to text) [00:10:41] [00:08:14].
  • Chunker: Splits large pieces of extracted context into smaller, manageable chunks for efficient similarity search [00:10:44] [00:08:20].
  • Tools Infrastructure: Enables agents to automatically call external APIs and connect to microservices [00:11:33] [00:05:40].
  • Workflow Engine: Purpose-built for multi-step agent processes [00:10:30].

When AI agents are built with predefined, highly scalable, and composable AI primitives, especially those with integrated cloud capabilities, the result is a serverless AI agent that can automatically handle heavy lifting [00:05:15].

Common AI Agent Architectures Built with Primitives

AI primitives enable the construction of 80% of even the most complex AI agents [00:22:26]. Here are several architectures implemented using primitives:

  • Augmented LLM: An agent that combines an LLM with tools, threads, and memory to generate output from input [00:11:18]. This versatile architecture can be used to build almost any type of AI agent [00:12:44].
  • Prompt Chaining and Composition: Involves using multiple agents (e.g., summary, features, marketing copy agents) that work together in a sequence, where the output of one agent determines the next step [00:13:06]. This is typically implemented with plain JavaScript or TypeScript [00:13:41].
  • Agent Router (LLM Router): An agent or LLM that decides which other specialized agent should be called next based on the input [00:14:02]. For example, a router can direct queries to a summary agent, reasoning agent, or coding agent, each potentially using a different LLM [00:14:24].
  • Parallel Agents: Allows multiple agents to run concurrently to achieve a task, such as a sentiment analysis, summary, and decision-maker agent all running at once [00:16:47]. This is straightforward to implement with Promise.all in JavaScript [00:16:55].
  • Agent Orchestrator/Worker: An orchestrator agent plans and creates subtasks, which are then distributed to multiple worker agents for execution [00:17:13]. The results from the worker agents are synthesized by another agent [00:17:24]. This architecture is common in deep research agents [00:17:30].
  • Evaluator/Optimizer: An agent generates a response (e.g., marketing copy), which is then evaluated by another LLM acting as a judge [00:20:11]. The judge accepts or rejects the response, providing feedback for improvement and iteration [00:20:25]. This evaluation agent should be built with the best possible LLM for the specific domain [00:21:14].
  • Memory Agent: Involves uploading data to a memory primitive and then using an agent to retrieve data and answer questions from that uploaded data [00:21:50].

By using AI primitives, engineers can focus on building powerful AI agents that are natively scalable and adaptable to new advancements, avoiding the limitations of bloated frameworks [00:26:08]. This approach simplifies the development-to-production pipeline, allowing rapid deployment and use of agents [00:26:39].

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