Agentic Engineering & Enablement
We build agents that do more than answer prompts: they use tools, reach context, follow rules and carry out tasks inside the boundaries the company has set.
An agent in production is distributed software: it needs identity, permissions, state, observability, cost control and failure handling. That is what separates an agent ready for production from a pilot that only works in the demo.
Outcome
Agents the company can actually operate, with defined permissions, measured behavior and the means to stop or roll back what they do.
What we do
The practice of taking agents to production and keeping them there is known as AgentOps. We structure the work around its four pillars.
Governance and security
We define access control, policies, data protection and guardrails. The agent gets only the permissions its task requires, regardless of who triggered it.
Build and operations
We design the architecture, tooling, integrations and deployment of agents for real production environments, including an identity of their own and session isolation.
Evaluation
We build tests that measure quality, behavior, safety and performance using cases from the company's own domain. The point is knowing whether a change actually improved the agent.
Observability and monitoring
We track agents, models, tools, cost and execution, keeping the trace needed to understand what happened and investigate problems.
Enablement for your team
AI-assisted engineering
We help the team bring tools such as Claude Code into the engineering workflow they already have, keeping technical review, tests and control over what reaches production.
Agent development by the in-house team
We teach how to design, version and review the components that define an agent: tools, context, rules and evaluation criteria.
Handover of operations
Documentation, joint review and support until the in-house team can run, evaluate and evolve the agents on its own.
How we work
We start from the task, not from the agent
Not every task needs an agent. When deterministic automation solves the problem, it tends to be simpler, cheaper and more predictable.
We set the boundary before the tools
We establish which systems the agent can reach, what permissions it holds and under what conditions it may take each action.
We connect tools through tool use and MCP
The agent reaches internal systems through paths the company already controls, without copying data unnecessarily or opening permissions wider than needed.
We measure before widening
We build an evaluation set with the team and run it on every meaningful change of prompt, tool or model.
We ship with a trace and a limit
We record the executions needed for investigation, set cost and execution limits, and keep a way to stop the agent without compromising the main system.
AWS services and tools involved
- Amazon Bedrock AgentCore
- AgentCore Runtime
- AgentCore Gateway
- AgentCore Memory
- AgentCore Identity
- AgentCore Observability
- AgentCore Evaluations
- AgentCore Policy
- MCP
- Anthropic Claude
- Claude Code
- Amazon CloudWatch
Where we usually come in
The agent works in the demo but not in practice
Pilots that hold up on the cases they were built for, then behave unpredictably the moment they get tasks or data outside the original script.
Nobody can say whether the agent improved
Teams adjusting prompts, tools or models without a consistent evaluation set, and trading one problem for another with every change.
The team wants to build with AI
Companies that want to bring agents and AI-assisted engineering tools into everyday work without giving up technical review, security and control over the software they produce.