Context that belongs to your business
Connect internal knowledge and business systems with access-aware retrieval and clear data boundaries.
Custom AI agents
Give your agent a clear purpose and a sound architecture.
Design Claude- and OpenAI-based agents around your knowledge, tools, and business rules. Start with architecture and evaluation, then add focused implementation support where needed.
Agent engineering
Built around a specific need.
A useful agent needs appropriate context, permitted tools, and a way to recognize when it should stop. We help your team specify those boundaries and evaluate behavior. A prototype or integration can then test a particular design decision before you expand the scope.
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.
HOW RESPONSIBILITY WORKS
We own agreed design and evaluation work. A prototype or integration has a separate scope; your team owns the wider rollout and operations.
Compare engagement options ↗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.
ILLUSTRATIVE ENGAGEMENT / NOT A CUSTOMER CASE STUDY
What this can look like in practice.
An internal assistant can answer questions in a demo, but must now work with restricted documents and business tools. The next step is to define whose permissions apply, which actions need approval, and what the agent should do when evidence is missing.
BEFORE WE START
What to bring.
- Representative tasks and examples of unacceptable behavior
- Knowledge sources and their access model
- Candidate tools, business rules, and approval owners
HOW WE ASSESS PROGRESS
What useful progress looks like.
- Task success can be checked against representative cases
- Actions stay within agreed permissions
- Missing evidence and tool failures have explicit fallback paths
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.
ARCHITECTURE / ADVISORY / ENGINEERING