A chatbot primarily responds to user questions or prompts. An AI agent can go further by carrying out multi-step tasks, retrieving information, using tools, interacting with business systems, and taking actions based on defined goals and rules. A conversational interface may be part of an agent, but conversation doesn't have to be its primary function.

Traditional workflow automation is best suited to predictable processes with clearly defined triggers and steps. AI agents can handle workflows that require interpretation, reasoning, or adaptation based on the information they encounter. In practice, the two can also work together, with AI handling judgment-oriented tasks while automation manages predictable actions and system integrations.

Custom development makes sense when an agent needs to understand your organization's specific knowledge, follow unique business rules, integrate with internal systems, or perform specialized workflows that a general-purpose AI product can't support effectively. If an existing product already meets the need, custom development may not be necessary.

AI agents can potentially integrate with CRM, ERP, document management, customer support, analytics, productivity, and other business systems that provide appropriate APIs or integration capabilities. The specific architecture depends on the systems involved, the information the agent needs, and the actions it is authorized to perf

Yes. A custom agent can be designed to retrieve and use approved organizational information such as documents, policies, product information, project data, customer information, or other knowledge sources. Access controls and permissions can be incorporated so the agent only uses information appropriate for its purpose and users.

That depends on the task, potential consequences, and level of risk involved. Some agents can safely complete routine actions independently, while others should recommend an action and wait for human approval. We design appropriate guardrails, permissions, escalation paths, and human checkpoints based on what the agent is being asked to do.

The timeline depends on the agent's complexity, integrations, data requirements, and level of autonomy. UpTop uses a phased process that moves from discovery and architecture through building, testing, deployment, and support. A focused agent performing a clearly defined job can typically be delivered much faster than a broader agent operating across multiple systems and workflows.

Testing evaluates more than whether the agent can complete its intended task. We also test how it handles incomplete information, unexpected inputs, system failures, permissions, edge cases, and situations that require human intervention. The goal is to establish reliable behavior and clear boundaries before the agent is trusted with real business processes.