Agentic AI services: AI agents built to run in production

AXIOMIO designs, builds and operates AI agents on AWS. An AI agent is a system that plans a task, calls tools and APIs to carry it out, checks the result and hands off to a person when it should. We build agents on Amazon Bedrock AgentCore with scoped identities, Model Context Protocol (MCP) tools behind a gateway, human approval for irreversible actions and full tracing, then run them with the same operations and security team that runs your AWS.

Frequently asked questions

What is agentic AI, and how is it different from a chatbot?

A chatbot answers. An agent acts: it plans a task, calls tools and APIs to carry it out, checks the result and decides the next step. That makes agents useful for multi-step work, and it is why they need identities, permissions, limits and monitoring like any other production system.

What is Amazon Bedrock AgentCore?

AgentCore is AWS's platform for running AI agents in production. It provides a managed runtime, memory, a gateway that turns APIs into MCP tools, agent identity, policy controls, observability and evaluations, and works with open-source agent frameworks and models inside or outside Amazon Bedrock.

How do you keep agents from doing something harmful?

Agents get their own least-privilege identity, a short list of approved tools, step and budget limits, and policies enforced outside the model. Irreversible actions such as payments, deletions or messages to customers wait for a person to approve. Every step is traced, and we test against prompt injection and tool misuse before launch.

Which processes are good candidates for AI agents?

Repetitive, multi-step work that follows rules but needs judgement on messy inputs, where outcomes are easy to check: triage, research and preparation, data gathering across systems, and routine back-office tasks. We avoid starting with work where a single error is costly and hard to reverse.

Do we need one agent or several?

Usually one, to start. A single agent with good tools is easier to test, trace and secure. We split into several specialised agents under a supervisor only when the task clearly needs different skills or permissions.

How do you monitor agents in production?

Every run is traced step by step, with tool calls, latency, token cost and outcome. We track task success, escalation rate and cost per task against agreed targets, alert on loops and anomalies, and review samples of runs. That is part of managed AI operations.

What does it cost to run an agent on AWS?

Running cost is mostly model tokens per task plus the runtime and tools. We estimate cost per task during the pilot, then control it with model routing, caching, step limits and budgets, and report it monthly.