KTM ONE Agentic AI Consulting Services on AWS

KTM ONE designs, builds and hands over production AI agents on AWS. Each agent ships with guardrails, human approval gates, spend caps, tracing and infrastructure as code, so it can act on your systems under controls you can audit.

KTM ONE helps organizations move from AI that answers questions to AI agents that complete work. We design, build, secure and hand over agentic AI systems on AWS, using Amazon Bedrock for foundation models and Amazon Bedrock Guardrails for policy controls. Agents are built with open frameworks such as the Strands Agents SDK and LangGraph and run on AWS Lambda, Amazon ECS on AWS Fargate or Amazon Bedrock AgentCore Runtime, depending on how long each task runs and how much state it carries. Agents reach your systems through tools exposed over the Model Context Protocol (MCP), and where one agent is not enough, specialist agents cooperate over the Agent2Agent (A2A) protocol.

Every engagement covers the same ground: a use-case assessment that separates tasks worth making agentic from tasks better left as fixed workflows; an architecture with defined tools, autonomy limits and human approval gates for consequential actions; the agent, its tools and its AWS infrastructure defined as code in your own account; an evaluation set and tracing so you can see what each agent did, what it cost and whether quality has moved; and runbooks and handover so your team can operate and extend what we build.

The Challenge

Many teams already use generative AI to summarize and draft, but the work that consumes time still needs a person to move between systems: triaging inboxes, reconciling documents, checking media assets, preparing reports. Moving to agents raises new questions that a chatbot never did. What may the agent do without approval? What does one run cost? How is it tested before it touches production data? How is it stopped if it misbehaves? KTM ONE answers those questions in the design, so the agent is useful and controllable on day one.

Use Cases

• Inbox and document agent: triages incoming email and documents, extracts key fields, drafts replies and routes consequential actions to a person for approval.
• Asset intake and reporting agent: ingests media assets, runs checks on each one and produces a report for the team.
• Document ingestion and query agents: a router agent sends each document to the right processing path, and query agents answer questions over the indexed library.
• Caption quality-assurance agent: reviews generated bilingual captions against the source audio and flags issues for human review.

Key Features and Differentiators

• Autonomy with limits: human approval gates, step caps and spend caps on every agent.
• Cost visibility: per-run cost tracked through tracing and tagging, so spend is visible before it becomes a surprise.
• Model strategy: a stronger model for planning and a lower-cost model for routine leaf tasks.
• Runtime fit: AWS Lambda, Amazon ECS on AWS Fargate or Amazon Bedrock AgentCore Runtime, chosen per workload rather than by default.
• Open protocols: tools exposed over MCP and agent-to-agent communication over A2A, so the design is not tied to one framework.
• AWS-native security: least-privilege IAM per agent, AWS KMS encryption and AWS CloudTrail audit logging.
• Delivered into your own AWS account with infrastructure as code (AWS CDK or Terraform).

Our Engagement Approach

1. Discover: select use cases, map the systems and data an agent would touch, set autonomy boundaries and success measures.
2. Design: agent architecture, framework and runtime choice, tool and MCP definitions, Guardrails policy, approval gates, cost caps and a security review of the design.
3. Build: implement the agent, tools and infrastructure as code, with an evaluation set built alongside.
4. Harden: test against realistic and adversarial inputs, simulate tool and model failures, load test, and confirm spend visibility and alarms.
5. Hand over: runbooks, dashboards, training for your team and a backlog for the next agent.