A chatbot answers questions. A managed agent completes work — with tools, approvals, and an operator who is accountable when something needs a human.
Most teams that ask for “an AI agent” already have a chatbot. The gap is not another conversation widget. It is a system that can take a defined job, use approved tools, stop when the next step is irreversible, and leave an audit trail.
This page is for operations and sales leaders evaluating managed autonomous agents — not a demo that lives in a slide deck. If you are still deciding whether you need consulting first, start with AI consulting services. If token and cloud spend is already the constraint, pair this with FinOps consulting.
A chatbot is a conversational interface. It retrieves answers, drafts replies, and hands the user back to a form or a human. That is useful. It is not an agent.
An agent is scoped to a workflow. It has a job (triage this queue, prepare this brief, update this record), a tool list (ticketing, CRM, email, knowledge base), and a stop condition. When the stop condition is “needs approval” or “data is incomplete,” it escalates instead of guessing.
If the work cannot be written as a job + tools + stop rules, you do not have an agent use case yet. You have a chatbot or a research assistant. That is a cheaper starting point — and it should stay that way until the workflow is clear.
Autonomy without a human-in-the-loop (HITL) design is just an unsupervised intern with API keys. Governance is the product.
HITL is not a person watching every token. It is a rule set for when a human must act:
Those gates are written before go-live. They live in the runbook, not in a Slack thread after the first incident.
If a vendor cannot show you those four items, you are buying a chatbot with extra steps.
Agent cost is not a single license line. It is two layers that move independently.
Model calls, embeddings, and tool traffic scale with volume and with how chatty the agent is. A poorly scoped loop can spend more than a well-scoped daily batch. Usage should be visible the same way you watch other GenAI spend — which is why agent work and FinOps belong in the same conversation.
The second layer is the work that does not show up on an invoice from the model vendor: workflow design, integration, prompt and tool policy, monitoring, and the people who handle escalations. That is the managed service.
We do not quote a fake “typical savings” number here. What you get instead is a cost map: which workflows are batch vs real-time, which tools are expensive, and which actions stay human so you are not paying for autonomy you do not want.
The commercial entry point is the same as the rest of the site: a paid Discovery call at $99. That session is for scoping the job, the tool list, and the operating model — not for a free-form demo.
Agents earn their keep where work is repetitive, tool-backed, and already has a definition of done. Two places that usually qualify:
We do not claim a named client or a percentage lift. The test is operational: can you point to a queue, a system of record, and a person who will own the exceptions? If yes, an agent is in scope. If not, start with consulting to pick the first workflow.
Product detail for the agent offering lives on autonomous AI agents.
A 30-minute Discovery call ($99). We map the job, the tools, and the approval gates — then you decide whether a managed agent is the right next step.
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