AI agents that can do the work—and respect the boundary.

Useful agents

Built around a job, not a generic chat window.

A useful agent has a defined role, the minimum context it needs, and a clear definition of a successful outcome. We map the work before choosing a model or framework, then connect only the tools and data sources required for that role.

The result can support research, document processing, service operations, internal knowledge, case preparation, or multi-step back-office work while keeping ownership with your team.

• Role and task-specific agent design
• Retrieval from approved knowledge sources
• API, document, CRM, and workflow integrations
• Model routing based on task and sensitivity

Controlled action

Every capability receives an explicit permission.

Agents that can send, update, approve, or publish require stronger boundaries than assistants that only draft. We separate read and write capabilities, scope credentials, validate tool inputs, and require human confirmation where consequences justify it.

Threat modeling covers prompt injection, untrusted content, data leakage, excessive agency, and unsafe recovery. Controls are made visible in the product rather than hidden in a system prompt.

• Least-privilege tools and scoped credentials
• Approval gates for external or irreversible actions
• Isolation of untrusted content and instructions
• Complete logs for agent decisions and tool calls

Production quality

Evaluation continues after the demo works.

We create representative test sets, quality criteria, and failure categories around the actual workflow. Model outputs, tool calls, latency, cost, and recovery behavior can then be measured before and after release.

Operational dashboards and audit trails help teams see what the agent attempted, what it used, what it changed, and when a human took control.

• Task-specific evaluation suites
• Quality, latency, and cost observability
• Fallbacks and graceful degradation
• Versioned prompts, policies, and workflows

Agent engineering in three controlled stages.

01 · Role and risk model

Define the job, information boundary, permitted tools, approval points, and unacceptable outcomes.

02 · Working vertical slice

Build one end-to-end workflow and measure it against representative cases before expanding scope.

03 · Harden and operate

Add evaluations, permissions, monitoring, recovery, documentation, and controlled rollout.

AI agent development questions

Which business systems can an AI agent connect to?

Agents can connect to systems that expose an appropriate API or controlled interface, including document stores, CRMs, ticketing tools, internal services, databases, and communication platforms. Each integration is scoped to the minimum required operations.

How do you keep an AI agent from taking unsafe actions?

We combine scoped credentials, deterministic policy checks, input validation, approval gates, isolated execution, and observable tool calls. The exact controls depend on the consequences and data sensitivity of the workflow.

Do you build with a specific model provider?

No single provider is required. We select or route models based on quality, privacy, latency, hosting, and cost requirements so the surrounding product is not unnecessarily locked to one vendor.

Related Sano Studio capabilities

AI Workflow Automation

Turn repeatable cross-system work into controlled, observable workflows.

AI Engineering Studio Berlin

Strategy and engineering support from discovery through production.

Give your next agent a real operating model.

Bring us the workflow, the constraints, and the systems involved. We will help you shape a secure path to a working agent.