[ 01 ]
Workflow agents
Agents that research, qualify, draft, reconcile, route, or operate across a defined business workflow.
CAPABILITY / 01 · Inside your MVP · AI that does useful work
AI Agent
Development
We build AI agents that can use tools, follow boundaries, recover from failure, and fit into real operations—not demos that collapse the first time the model improvises.
AI agentsMulti-agent systemsTool useEvalsHuman approval
Agent-native
Architecture, not a chat wrapper
Human-in-loop
Control where consequences matter
Evaluation-led
Reliability measured before launch
/01 — What we build
Useful autonomy, with boundaries.
The right agent is not the one that does the most. It is the one that reliably completes the intended work, asks for help at the right moment, and leaves an audit trail your team can trust.
[ 02 ]
Multi-agent systems
Specialized agents coordinated through explicit roles, shared context, and predictable handoffs.
[ 03 ]
Tool integrations
Controlled access to APIs, databases, CRMs, internal services, search, files, and approved actions.
[ 04 ]
Evaluation systems
Scenario suites, quality rubrics, regression checks, traces, and failure analysis built into delivery.
[ 05 ]
Guardrails + approvals
Permission boundaries, budgets, moderation, policy checks, and human confirmation for sensitive actions.
[ 06 ]
Production operations
Observability, retry strategy, fallbacks, model routing, cost controls, and maintainable deployment.
/02 — Delivery system
From workflow to dependable agent.
We prove the behavior before scaling the interface. Every stage creates something testable, so reliability grows with the product instead of becoming a late-stage rescue mission.
01
Map
Define the job, tools, inputs, boundaries, failure costs, and where a human must stay in control.
02
Prototype
Build the smallest complete loop and test it against representative scenarios and edge cases.
03
Integrate
Connect real systems, permissions, memory, observability, and the interface around the agent.
04
Operate
Launch with evaluations, traces, fallbacks, and a clear path for improving behavior safely.
/03 — Questions
Before we begin.
Clear scope, a shared launch target, and direct answers before the build begins.
Do we need a multi-agent system?
Often, no. We begin with the simplest architecture that can complete the workflow reliably. Multiple agents are useful when roles, permissions, context, or evaluation genuinely benefit from separation.
Can the agent use our existing tools and data?
Yes, when the systems expose a suitable API, database, file, or controlled browser workflow. We design permissions so the agent can access only what the job requires.
How do you reduce hallucinations and bad actions?
We combine grounded context, constrained tools, structured outputs, scenario-based evaluations, confidence thresholds, fallbacks, and human approval for consequential steps.
Can you deploy with multiple model providers?
Yes. When it helps resilience, quality, privacy, or cost, we design a provider-aware routing layer without coupling the whole product to one model.
/04 — Related proof
See the system in context.
Explore the products and adjacent capabilities that show how this work connects to a complete, launchable experience.
Your MVP.
Live in 30 days.
We get your MVP live in 30 days or you don't pay. Tell us your idea and who it is for. We will reply within one business day to help shape your launch plan.
Let's launch your MVP ↗Scope, launch criteria, and kickoff agreed before the build. How the guarantee works.