AI feature under review before shipping to production

Solution

AI Agents & Agentic Workflows

For product and engineering leaders adding an AI agent or agentic workflow to a product that already has real users, where a bad rollout has a real cost.

Confidence thresholds and fallback behavior for an AI feature

The Problem

An AI agent that carries out several steps of a workflow is different from a single prompt: it has to handle real traffic, real edge cases, and a cost per request that scales with usage. It also has to fail safely when it's uncertain or a step goes wrong. We design the agent with clear confidence thresholds, a defined handoff to a person when it shouldn't proceed alone, and monitoring that catches quality drift before your customers do.

Our Approach

Add an AI agent to your product without putting what already works at risk.

Scope

We define exactly what the feature should do, and where it should defer to a person instead of guessing.

Build

We build the feature with fallback behavior and cost guardrails included from the start, not bolted on later.

Test

We test it against real edge cases and failure modes before it reaches production traffic.

Monitor

We put quality and cost monitoring in place so drift gets caught early, with a clear owner for the alert.

What You Get

Deliverables.

A scoped feature spec: what the AI should decide, and what it should always leave to a person

Confidence thresholds and a defined fallback path for low-confidence or failed responses

Cost and rate-limit guardrails, so usage doesn't create a surprise bill

Quality monitoring that flags drift after launch, not just at demo time

The Outcome

The AI feature ships into your live product with a defined fallback, controlled cost, and monitoring in place, instead of a demo that breaks under real usage.