AI/ML
AI Agent
What AI agents are and how they work
Definition
An AI agent is a software system powered by a large language model that autonomously plans and executes multi-step tasks by calling APIs, querying databases, and making decisions. 1Raft builds production AI agents with human-in-the-loop checkpoints for high-stakes workflows across healthcare, fintech, and enterprise operations.
How it works
A chatbot answers questions. An AI agent gets things done. When you tell an agent to "process this insurance claim," it reads the document, extracts key fields, checks them against policy rules, flags discrepancies, and routes the claim for approval - all without you managing each step. The agent decides what to do next based on what it learns along the way.
Under the hood, an AI agent combines an LLM (for reasoning) with a tool-use layer (for action) and a control loop (for sequencing). The LLM receives a goal, breaks it into steps, selects the right tool for each step, interprets the result, and decides what to do next. This observe-think-act cycle repeats until the goal is achieved or the agent escalates to a human.
The challenge is reliability at scale. Agents that work in demos can fail in production when they encounter edge cases, ambiguous inputs, or tool errors. Production-grade agents need structured output validation, retry logic, token budgets, observability traces, and clear escalation paths for situations the agent cannot handle autonomously.
How 1Raft uses AI Agent
1Raft builds AI agents for clients across healthcare, fintech, and sales operations who need automation beyond simple prompt-response patterns. For a healthcare platform, we built an agent that triages patient intake forms, extracts symptoms, cross-references medical history, and routes cases to the appropriate specialist - with human-in-the-loop approval before any clinical action is taken. Every agent we ship includes structured guardrails, token budgets, and observability from day one.
Related terms
AI/ML
Agentic AI
Agentic AI refers to AI systems that can plan, make decisions, and take actions autonomously to achieve a goal. Unlike simple chatbots that respond to one prompt at a time, agentic systems break complex tasks into steps, use tools, and self-correct along the way.
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Prompt Engineering
Prompt engineering is the practice of crafting and optimizing the instructions given to a language model to get consistent, high-quality outputs. It is the most accessible and cost-effective way to improve AI application behavior without modifying the underlying model.
AI/ML
Model Inference
Inference is the process of using a trained AI model to generate predictions or outputs from new inputs. When you send a prompt to an LLM and get a response, that is inference. It is where compute costs, latency, and user experience are determined.
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
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