$ cd ~/services/ai-agents
AI agents on AWS.
Turn a repeatable business task into a workflow you can trust and measure.
Bring the product or workflow question you need to solve first. We’ll use the call to identify a practical next step.
Plan the workflow before the build.
Start with the task, the data, and the decisions a person should keep. We build AI-assisted workflows with explicit tool permissions, evaluations, monitoring, and a fallback when the model cannot finish the job.
Reference workflow
Give the agent a bounded task, then check the result.
Set the task
Provide the request, approved data, and a clear success criterion.
Use approved tools
Keep model reasoning inside explicit tool and data permissions.
Review the result
Validate the output, request approval when needed, or route to a person.
Across the workflow: evaluation cases, traceable actions, cost limits, and a fallback path.
Need an engineer to own ongoing production AI? Explore the Forward-Deployed Engineer offer →
# scope-and-delivery
Know what the work includes.
- A workflow map separating rules from model decisions
- Versioned prompts, tool contracts, and access controls
- An evaluation dataset and agreed acceptance checks
- A staged rollout with human review where needed
- Monitoring, cost limits, and fallback behavior
Data access, model selection, acceptable errors, and human approval rules are defined before implementation. Usage fees and ongoing evaluation work depend on the agreed workload.
A good fit when you have…
- A repeated task with accessible data and a clear definition of success
- An AI prototype that needs evaluation and operating controls
- A team ready to retain human review for consequential actions
Tools selected for the scope
- Amazon Bedrock
- AgentCore
- MCP tool interfaces
- Step Functions
- Retrieval
- Evaluation datasets
- Cost and latency monitoring
# before-you-start
The practical questions.
Does the task need an AI agent?
Not always. A deterministic workflow may be simpler when the rules and inputs are predictable. We use the consultation to identify where model judgment would add value.
How do you reduce wrong answers or unsafe actions?
We define allowed tools, validate inputs and outputs, test representative tasks, and retain approval steps for consequential actions. No model or guardrail removes all risk; the workflow must handle failure.
Can the agent use our internal systems?
That depends on the available APIs, permissions, and data requirements. We agree what the agent may read or change and build the integration around those boundaries.
Do you offer ongoing AI ownership?
Yes. The Forward-Deployed Engineer offer is for recurring production AI work, including evaluations, guardrails, and operations. A fixed-scope first workflow and an ongoing role are separate engagement options.
# your-next-step
Know what to build first.
A free 30-minute conversation about your scope, budget, and next step. A fixed-price proposal follows confirmed scope.
For this conversation, we’ll cover:
- The workflow you want to improve and its current cost
- The data and tools the agent would need to use
- A bounded first use case and how we would test it
Prefer to share your brief first? Send project details →