Context that belongs to your business
Connect internal knowledge and business systems with access-aware retrieval and clear data boundaries.
Custom AI agents
Agents designed around the way your business works.
Connect models, tools, and enterprise knowledge in agents with a defined purpose, explicit permissions, and human checkpoints.
Agent engineering
Built around a specific need.
A useful agent needs more than a prompt. It needs relevant context, a small set of permitted actions, and a way to recognize when it should stop. We engineer that complete system around the task, the people who own it, and the tools it must work with.
Discuss custom ai agents↳
01 / CONTEXTInternal knowledge
YOUR SYSTEM BOUNDARY⌘
✳CUSTOM AGENT + ORCHESTRATIONRetrieve & reason
ModelsToolsContext
✓CONTROL POINTSource validation
↗BUSINESS OUTCOMEGrounded answer
Source permissions · Sensitive-data controls
↺ Evaluation Observability Continuous improvement
WHAT THIS SERVICE COVERS
Tools with explicit permissions
Define which actions an agent can take, when it must ask for approval, and how it handles exceptions.
Evaluation before expansion
Test task completion, failure cases, and model behavior against the workflow before extending its scope.
ENGAGEMENT OUTPUTS
Something concrete
to build on.
↗Agent and tool specifications
↗Context and integration architecture
↗Evaluation scenarios and approval boundaries
The scope and deliverables are agreed for your engagement.
A PRACTICAL START
Give the agent a job it can be evaluated against.
A bounded agent for one operational workflow, with clear inputs, outputs, and ownership.
Bound the task
Define successful outcomes, prohibited actions, supported inputs, and escalation paths with the workflow owner.
Connect context and tools
Design retrieval, tool contracts, authentication, and authorization. Separate information access from permission to act.
Evaluate and operate
Build test cases for normal and adversarial inputs. Track task completion, incorrect actions, latency, and cost before expanding access.
BEFORE WE BEGIN
Your questions,
answered.
Can an agent use our private knowledge?
Yes, subject to the access and deployment model agreed for the project. Retrieval must respect source permissions, and the agent should retain references to the evidence it uses.
What happens when a model is uncertain?
We define fallback behavior for missing evidence, tool failures, and ambiguous requests. Depending on the workflow, the agent asks for clarification, stops, or routes the task to a person.
FROM IDEA TO IMPLEMENTATION