Solutions · Use case

Agentic Workflows.

Generative AI, scoped to the shape of the problem it solves.

Multi-step LLM agents that propose actions inside existing operational workflows.

Use-case context

Agentic workflows are the use-case most often oversold and least often deployed safely — every vendor demo shows an agent booking flights, but the production-grade versions live inside operational workflows like exception handling, network-incident triage, and provisioning. The hard problem is bounding what the agent can touch and ensuring every proposed action has a clear human-approval surface. We build agents scoped to a specific operational workflow with explicit tool boundaries, structured action-proposal output, and human-in-the-loop approval at every state-changing step. Tools are typed, idempotent where possible, and instrumented so every agent decision is replayable from logs. Measured by task-completion rate on a labelled set of multi-step scenarios, plus a safety-rate metric (proportion of runs in which the agent stayed inside its tool boundary and produced a valid approval payload). Time-to-resolution and operator-approval rate are tracked so that low-quality proposals are visible. We do not deploy agentic workflows that execute state-changing actions on production systems without a logged human-approval step — the agent proposes, an operator commits, and the audit trail captures both sides. In our coverage footprint this lands first across telco, technology, logistics — the sectors where the data shapes and evaluation criteria line up cleanly with what this use-case actually measures.

Data shape

Operational runbooks, workflow definitions, the typed tool catalogue exposed to the agent, historical incident or exception traces, and the existing ticketing or workflow engine the agent must integrate with.

Where we draw the line

We do not deploy agentic workflows that execute state-changing actions on production systems without a logged human-approval step — the agent proposes, an operator commits, and the audit trail captures both sides.

Talk to us about a agentic workflows engagement

A 30-minute call to scope where agentic workflows actually moves the curve against your evaluation criteria.

Book strategy call

Why work with Veso AI on agentic workflows

Measured

Evaluation, not opinion

Measured by task-completion rate on a labelled set of multi-step scenarios, plus a safety-rate metric (proportion of runs in which the agent stayed inside its tool boundary and produced a valid approval payload). Time-to-resolution and operator-approval rate are tracked so that low-quality proposals are visible.

Fixed-fee

After paid discovery

Two-week discovery assembles the labelled evaluation set with your subject-matter experts, then converts into a fixed-fee proposal with explicit gates.

Your repo

Your IP, day one

Code, infrastructure-as-code, evaluation harness, and runbooks land in your accounts — no vendor lock-in on the data, models, or evaluation history.

Related use-cases

FAQ

Agentic Workflows — frequently asked questions

How is success measured for agentic workflows engagements?

Measured by task-completion rate on a labelled set of multi-step scenarios, plus a safety-rate metric (proportion of runs in which the agent stayed inside its tool boundary and produced a valid approval payload). Time-to-resolution and operator-approval rate are tracked so that low-quality proposals are visible. The evaluation harness is part of the deliverable, not an afterthought — we build it during the engagement so your team can run it against the next prompt, model, or pipeline change without us.

Where does Veso AI NOT apply agentic workflows?

We do not deploy agentic workflows that execute state-changing actions on production systems without a logged human-approval step — the agent proposes, an operator commits, and the audit trail captures both sides. This is a deliberate trust boundary, not a capability gap — we are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in those surfaces does not justify it.

Which industries does agentic workflows apply to?

In our coverage footprint, agentic workflows most commonly lands in telco, technology, logistics, education. The specific deployment shape varies by industry — data shapes, evaluation criteria, and regulators differ enough that we re-scope each engagement against the sector it lands in.

What data shape do you need to start a agentic workflows engagement?

Operational runbooks, workflow definitions, the typed tool catalogue exposed to the agent, historical incident or exception traces, and the existing ticketing or workflow engine the agent must integrate with. During the paid two-week discovery we map the actual data surface — what exists, what is labelled, what residency posture it carries — and the proposal for the next gate is shaped against that, not against an assumption.

Which Veso AI services ship agentic workflows?

agentic workflows ships under our Generative AI Consulting, Custom Software Development, AI Integration Services service lines, depending on the integration surface and the build-vs-platform trade-off. Most engagements draw on more than one — the boundary between consulting, custom build, and integration is a scoping decision we make explicit during discovery.

How does a agentic workflows engagement typically start?

With a paid two-week discovery: workshops with leadership and operators, an evaluation-set assembled with your subject-matter experts, and a fixed-fee proposal for the next gate. The evaluation set anchors every subsequent decision — model choice, prompt strategy, retrieval design — so quality is measurable from week one, not from go-live.