Understand the goal. Complete the work.

Agentic AI describes AI systems that don't just generate answers; they understand a goal, plan the steps to reach it, make decisions, connect to your systems, and complete the work. Instead of returning text for a person to act on, an AI Agent acts within defined limits to finish the task.

An AI Agent interprets the request, breaks it into steps, and decides what to do at each one. It retrieves the right knowledge, calls the systems and tools it is permitted to use, checks its own confidence, and escalates to a person when needed, producing an outcome rather than just a response.

Generative AI produces content such as text, summaries, and answers. Agentic AI uses that capability to take action: it plans, decides, calls systems, and completes tasks end to end. Generation is one tool an AI Agent uses; the AI Agent is defined by what it gets done.

A traditional chatbot follows scripted paths and answers questions. An AI Agent understands intent, reasons about the goal, connects to live systems, and completes the transaction, such as opening a request, updating a record, or resolving a case, rather than only pointing the customer to the next step.

It is the foundation that lets an organization build, govern, deploy, and operate AI Agents at scale, with orchestration, system integrations, knowledge and retrieval, security, monitoring, and deployment control. CBOT provides this as a single platform.

Orchestration is the layer that coordinates how an AI Agent moves through a task: which steps run, which tools and systems are called, when knowledge is retrieved, and when a person is brought in. It turns individual capabilities into a reliable, repeatable process.

CBOT AIFlow is the visual orchestration layer that helps enterprises design, govern, and operate Agentic AI workflows. It connects reasoning models, enterprise knowledge, tools, APIs, business rules, approvals, and human decision points in one controlled environment. AI Agents can understand the goal, plan the next steps, use the right systems, and move the process toward a completed business outcome.

Multi-agent orchestration coordinates several specialized AI Agents working together, with one handling identity, another a transaction, another knowledge, under a controlling layer that routes the task and keeps context consistent across them.

Yes. With the right permissions and integrations, AI Agents act in your systems, creating, updating, and completing records and transactions, within guardrails and, for high-impact steps, behind human approval.

Yes. The same AI Agent logic operates across voice and digital channels, so intent, context, and outcomes stay consistent whether the customer is on a call or in a messaging channel.

AI Agents carry context through a task and across steps, so earlier information isn't lost mid-interaction. They combine that working context with your enterprise knowledge and live system data to act on accurate, current information.

AI Agents are grounded in your approved knowledge through retrieval, constrained by guardrails, and required to act through defined tools and systems rather than inventing answers. Confidence checks and escalation catch uncertainty before it reaches the customer.

Yes. You decide where people stay in control. Confidence-based escalation hands off uncertain cases, and approval workflows require human sign-off before high-impact actions are carried out.

Yes. CBOT is model-flexible. You can use CBOT LLM, leading commercial models, or your own and open models, and orchestrate across them based on the task, cost, and where they need to run.

LLM orchestration routes each task to the most suitable model and coordinates how models, knowledge, and tools work together, so you can balance quality, cost, latency, and deployment constraints rather than depending on a single model.

Yes. The full stack, including inference, knowledge and retrieval, CBOT Speech, AIFlow orchestration, and the AI Agent runtime, can run inside your own infrastructure, behind your firewall, with no dependency on external services.

In banking, AI Agents handle identity verification, account and card servicing, payments and disputes, collections, application status, and customer support, completing the request in core systems under strict governance, across voice and digital channels.

In insurance, AI Agents support quoting, policy servicing, claims intake and status, document handling, renewals, and customer support, connecting to policy and claims systems to move each case forward rather than only answering questions.

Both. With CBOT AIFlow's visual environment, business teams can design and adjust flows and guardrails, while technical teams handle deeper integrations, so changes don't wait on long development cycles.

Through real-time operational monitoring and a full execution trace: you can see resolution and escalation rates, latency, and outcome quality, replay how a task was handled, and get alerts when behavior drifts.

Most enterprises move from scoping to a live, governed AI Agent in weeks. CBOT's connectors, delivery method, and AIFlow environment remove much of the build effort that normally stretches projects into quarters.

Start with a focused use case and a tailored demo. We map it to your systems and governance, stand up a governed AI Agent, and expand from there. Request a demo or talk to an AI expert to scope your first AI Agent.