Machine Learning Competency
Practice area covering applied AI that acts inside a product rather than alongside it — agent tooling, evaluation harnesses, and inference cost discipline.
- In progress
- In progress
- —
- Pending approval
What it covers
Agents that act, not sidebars that suggest.
Our submission centres on production agentic systems — where the model invokes the same capabilities the interface does, and every action is auditable.
Agent tooling
Shared capability registries called by both the model and the UI.
Evaluation harnesses
Measurable agent behaviour, so model changes are testable rather than felt.
Inference discipline
Model selection and routing tuned against real cost and latency budgets.
Auditable actions
Every agent-invoked call recorded against the workspace history.
Validation criteria
What AWS assessed.
The criteria AWS assesses for this competency, and our current status against each. Status is maintained in the CMS from AWS Partner Central.
How validation worksProduction workloads
Two AI-native products running on Bedrock with real users.
Customer references
Named engagements with contactable clients and full architecture detail.
Technical review
AWS solutions architects review the agent and inference architecture.
Final validation
AWS's decision once the technical review is complete.
Evidence
The engagements behind it.
These are the customer references submitted to AWS for this competency. Ask us about any of them.
24
Executable agent tools
Seshira — shared tool registry
One capability registry called by both the agent and the interface, with every invocation auditable.
Native order channel
Mercado — AI order parsing
Free-form chat messages converted into structured, priced order lines that land reconciled.
Architecture
Patterns behind this practice.
Services
What we build it on.
- Bedrock
- Bedrock AgentCore
- SageMaker
- Lambda
- DynamoDB
- EventBridge
Team on this practice
Applied AI runs out of the engineering bench, with the practice lead who built Seshira's agent layer.
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Work with this practice
Building AI that actually does something?
Discovery comes first — a short, bounded review that ends in an architecture, a scope, and a price. You keep the output either way.