AI/ML
Agentic AI
What agentic AI is and why it matters
Definition
Agentic AI describes artificial intelligence systems that operate with a degree of autonomy - planning multi-step tasks, using external tools, making decisions, and self-correcting to achieve defined goals. Unlike reactive chatbots, agentic AI can orchestrate workflows, call APIs, search databases, and iterate on results without human intervention at each step.
How it works
Traditional AI takes an input and produces an output. Agentic AI takes a goal and figures out how to accomplish it. An agentic system might receive the instruction "research these five competitors and produce a comparison report," then autonomously search the web, extract data, structure findings, and generate the final document.
Agentic architectures typically combine an LLM (for reasoning and planning) with a set of tools (APIs, databases, code execution environments) and a control loop that checks whether each step succeeded. If the agent gets bad results from one approach, it can try another. This makes agentic systems far more capable than single-prompt interactions.
The trade-off is reliability. More autonomy means more ways things can go wrong. Production agentic systems need guardrails: token budgets, approval checkpoints for high-stakes actions, structured output validation, and observability to trace what the agent did and why.
How 1Raft uses Agentic AI
We build agentic AI systems for clients who need multi-step automation beyond simple prompt-response patterns. In fintech, we built an agent that monitors transaction patterns, flags anomalies, pulls additional data from internal systems, and drafts compliance reports. We design agents with clear guardrails and human-in-the-loop checkpoints for any action with real-world consequences.
Related terms
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
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.
AI/ML
MLOps
MLOps (Machine Learning Operations) is the set of practices for deploying, monitoring, and maintaining machine learning models in production. It applies DevOps principles to ML systems, keeping models accurate, reliable, and cost-effective after launch.
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.
Related services
Next Step
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