Fission Labs Agentic Systems Engineering on AWS

Agent demos are easy. Agents you can trust with real systems are not. Fission Labs engineers production agentic systems on AWS that reason, coordinate, use your enterprise tools, and execute business workflows, with human oversight and deterministic controls on every consequential action.

Why agentic engineering on AWS Most agent pilots stall on reliability, integration, and control: unpredictable behavior, no audit trail, and no safe way to let an agent write to a system of record. AWS provides the building blocks in Amazon Bedrock and Amazon Bedrock AgentCore, and Fission Labs, an AWS Advanced Tier Services Partner with the AWS Generative AI Competency, turns them into governed systems that run in production.

What you get: agents that act safely

• Use case selection: high value agentic use cases identified and scored for feasibility, risk, and return
• Multi agent orchestration: specialized agents coordinated with LangGraph, CrewAI, AutoGen, or Amazon Bedrock, matched to the workflow
• Enterprise integrations: tool calling and Model Context Protocol connections to systems such as Jira, Salesforce, and ServiceNow
• Memory and context: short and long term memory so agents work with the right information at each step
• Human in the loop approvals: checkpoints where people review, approve, or override agent proposals
• Deterministic policy gates: the model proposes, and versioned application logic decides whether a consequential action is permitted
• Observability: tracing, evaluation, and cost visibility across every agent step

Proven results: Omada Omada needed campaign content at a scale headcount could not match. We built an orchestrated multi agent system on Amazon Bedrock with four coordinated agent types on a serverless backbone that scales across tenants.

• 85% reduction in content production time
• 10x increase in marketing output

Security and governance Agents run in your AWS account with least privilege access through AWS IAM, and write credentials are held by the policy gate, not the model. Every agent action is logged, so you can show what the system did and why.

How we engage

• Discover: select and prioritize use cases with your business and technical owners
• Design: define agent roles, orchestration, integrations, and the control model
• Build: develop agents, tools, memory, and policy gates, accelerated by FloTorch
• Validate: test behavior, safety, and cost against agreed acceptance criteria
• Deploy and hand over: release to production with observability, runbooks, and training

Who this is for

• Teams moving from an agent pilot to production
• Enterprises automating workflows across systems of record
• Leaders who need human oversight and auditability for autonomous AI

Get started Contact us for a free scoping call to review your use cases, systems, and control requirements.