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AI consulting use case

AI consulting for agentic workflow automation

Agents that carry multi-step work across your systems end to end — bounded, evaluated, and observable enough to trust in production.

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The problem

Why agentic workflow automation is hard to get right

A lot of work is multi-step and spread across several systems: a request arrives, someone checks three tools, copies data between them, applies judgment, and files a result. It is too variable for a rules engine and too repetitive for senior people, so it sits in a queue. The temptation is to point an autonomous agent at it; the hard part is bounding what the agent can touch, proving it does the right thing on real cases, and being able to see every step it took when something goes wrong.

How we build it
01
A tight, well-bounded tool surface
We define exactly which systems and actions the agent can reach, with permissions and reversibility designed in — so its autonomy never exceeds what the task actually needs.
02
Evaluation against real cases
We test the agent on your real historical workload, not demos — measuring completion, error rate, and the cases where it should stop and ask, before it touches anything live.
03
Human-in-the-loop where it earns its place
High-stakes or low-confidence steps route to a person while routine ones run unattended; the boundary is explicit and widens only as the evaluations hold.
04
Full observability of every step
Every decision, tool call, and output is traced, so you can audit what the agent did and why — and intervene the moment its behavior drifts.
The outcome

A narrow workflow that runs end to end without a person in every step — and a complete trace you can stand behind when someone asks exactly what it did.

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Key concepts
GlossaryAI agentsAn AI agent is a system that uses a language model to decide and take actions toward a goal — calling tools, observing results, and looping until the task is done.
GlossaryguardrailsGuardrails are the controls placed around an AI system that constrain what it can say or do — checking inputs and outputs and bounding the actions it's allowed to take.
GlossaryevalsEvals are structured tests that measure how well an AI system performs against a set of real tasks, turning model quality from a feeling into a number you can track.
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Put AI to work on agentic workflow automation

Bring us the metric you need to move. We will tell you what we would build, how long it takes, and what it is worth.

See our work