How do you keep agents reliable?

Durable workflow state, explicit retries and fallbacks, and evaluation against real cases, rather than relying on a single long prompt.

Can a person approve actions before they happen?

Yes. We add human approval gates at the decisions that matter, with a full audit trail of what ran and why.

How do you control cost?

Routing, caching, and per-workflow budgets keep inference cost predictable even as usage grows.

Capabilities

Agents and Automation

We build agents for workflows that are worth automating and consequential enough to need guardrails. State, approvals, and cost controls come first, so automation is dependable rather than impressive.

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Problems we solve

A multi-step workflow is repetitive but too consequential to automate carelessly.
Early agent experiments are unreliable and hard to debug.
Autonomous actions need a human check before anything irreversible happens.

What's included

Agent architecture
Tool integrations
Workflow state
Human approval
Retry and fallback handling
Cost controls
Evaluation

How we approach it

01
Model
Map the workflow, its state, and the decisions that need a human.
02
Build
Implement agents with tool use, retries, and fallbacks.
03
Guard
Add approval gates, cost controls, and an audit trail.
04
Evaluate
Test against real cases and monitor reliability in production.

Representative technologies

LangGraphWorkflow enginesTool/function callingQueuesProvider-agnostic model APIsObservability

Frequently asked

How do you keep agents reliable?: Durable workflow state, explicit retries and fallbacks, and evaluation against real cases, rather than relying on a single long prompt.
Can a person approve actions before they happen?: Yes. We add human approval gates at the decisions that matter, with a full audit trail of what ran and why.
How do you control cost?: Routing, caching, and per-workflow budgets keep inference cost predictable even as usage grows.

Proof

Live demo