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DEVELOPERS

Integration is
where AI gets useful.

Practical considerations for connecting enterprise knowledge, models, and tools to your applications.

INTEGRATION ARCHITECTURE

A deliberate boundary
between reasoning
and action.

Define what an agent can read, what it can recommend, and which actions need approval. Build the integration around those boundaries.

ILLUSTRATIVE PSEUDOCODE
request = receive_business_request()
context = retrieve_authorized_knowledge(request)
data = read_permitted_system_data(request)
proposal = prepare_recommendation(context, data)

if human_review_required(proposal):
    decision = request_human_review(proposal)
else:
    decision = apply_agreed_policy(proposal)

if decision.is_approved:
    prepare_permitted_next_step(proposal)
record_outcome()

01 / API CONTRACTS

Define the integration contract.

Map the systems involved, agree on inputs and outputs, and specify permissions, validation, and failure behavior.

02 / SDK INTEGRATION

Fit your application environment.

Plan client libraries and integration code around your existing stack, supported interfaces, and deployment requirements.

03 / REFERENCE PATTERNS

Make the workflow explicit.

Separate information retrieval, recommendation, approval, and execution. Use clear handoffs to make each stage easier to evaluate.

04 / INTEGRATION GUIDANCE

Start with the right questions.

Which systems are accessible? What may an agent read or change? Who approves actions? How are outcomes evaluated and recorded?

Discuss project-specific API documentation, SDK integration, and reference implementation needs with our team ↗.

THE NEXT STEP

Let’s build AI
around your business.

Start with a workflow, a challenge, or a question.
We’ll explore what a useful implementation could look like.