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Capilano AIby Quanteroun Solutions

Custom AI Agents

Build an AI agent around one valuable job—not a vague promise of autonomy

Build task-focused agents that use approved knowledge, call business tools, follow guardrails, and hand work to people when judgment is required.

Best suited for
Operations and service teams with a repeatable, tool-enabled workflow
Engagement shape
Pilot through production deployment
Custom AI Agents

The service in practice

Start with the work that needs to improve

A useful enterprise agent understands a bounded task, works with approved evidence, uses tools under explicit rules, and knows when to stop or involve a person. That operating design matters more than the number of agents in the architecture.

Capilano AI designs, builds, and integrates custom agents across cloud and business platforms. We treat instructions, tools, identity, retrieval, approval, evaluation, telemetry, and ownership as one production system.

Why this work matters

Replace operational friction with a service your team can trust

What is getting in the way

The job is underspecified

A broad assistant brief hides the decisions, evidence, permissions, and completion criteria the agent actually needs.

Tool access creates operational risk

An agent that can update systems, send messages, or trigger workflows needs scoped identity, validation, and approval boundaries.

A good demo masks inconsistent behavior

Happy-path examples do not reveal wrong tool arguments, missing context, retrieval failures, unsafe actions, or escalation gaps.

What the engagement should change

A task-focused agent design

Clear inputs, outputs, tools, decision boundaries, human roles, failure handling, and completion criteria.

Secure integration with real systems

Identity-aware access to knowledge, CRM, ticketing, scheduling, data, messaging, or internal APIs.

Evidence for a production decision

A representative test set, task and tool-call evaluations, traces, risk findings, and a rollout recommendation.

Where to apply it

Start with a bounded operating outcome

Each use case is scoped with data access, integration, evaluation, human approval, monitoring, and ownership from the beginning.

Operations case agent

Collect context, retrieve policy, update a case, prepare a decision, and escalate exceptions.

Sales and service research agent

Assemble approved customer, product, and account context before a human conversation.

Document-to-action agent

Interpret validated document data and trigger the appropriate workflow with approval controls.

Internal knowledge agent

Answer role-specific questions with citations and permission-aware access to company content.

How we deliver

Evidence before scale. Ownership before launch.

The method is intentionally practical: reduce uncertainty early, build the full operating path, and leave the service with people who can run it.

See our delivery approach
  1. 01

    Define the operating outcome

    Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.

  2. 02

    Prove the difficult assumptions

    Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.

  3. 03

    Build the complete service

    Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.

  4. 04

    Release with an owner

    Roll out in controlled stages, train the operating team, review production evidence, and convert confirmed failures into improvements.

Typical engagement

What your team receives

The exact scope follows the operating outcome and current environment. These are the core work products typically required to make the result useful and supportable.

Agent workflow and tool design

Model and orchestration selection

Identity-aware system integrations

Human approval and escalation paths

Task, tool-call, and safety evaluation

Production deployment and team handoff

Questions to resolve early

Frequently asked questions

Do we need a multi-agent system?

Usually not at first. We begin with the smallest architecture that can complete the job reliably. Multiple agents are useful only when role separation or independent control boundaries clearly improve the workflow.

Which models and frameworks do you use?

We choose after defining the task, deployment environment, data controls, latency, evaluation target, and team skills. The architecture should make critical business logic portable where practical.

Can an agent safely update our systems?

Yes, when actions are narrow, identity is scoped, arguments are validated, sensitive steps require confirmation, calls are logged, and recovery paths are tested.

How do you evaluate an agent?

We measure task completion, evidence use, tool selection, tool arguments, policy compliance, escalation, latency, and cost on representative scenarios—including failures and ambiguous requests.

Field notes

Read the implementation detail

All insights

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Bring us the workflow—not a finished AI specification

We will help clarify the operating outcome, difficult assumptions, delivery path, and evidence required for a responsible investment decision.