AI Agent as a Service

Agent as a service is a scoped implementation offering in which an AI agent is designed, connected to approved information and tools, tested against representative work, and handed over with operating guidance. Rather than selling a generic assistant, Benian defines a specific business job, its completion standard, human approval points, exception handling, and measurement approach. The result is a purpose-built system aligned with your process.

An AI agent can read approved information, extract fields, classify requests, prepare an intake brief, research a defined prospect using permitted sources, summarize conversations, highlight open decisions, draft content, or prepare a task in an existing work system. The appropriate scope depends on available data, tool access, review requirements, and the cost of an incorrect output. Irreversible or consequential actions require explicit approval rules.

A chatbot primarily supports conversation, such as answering questions or collecting an enquiry. An AI agent is designed around a repeatable business task and may retrieve information, interpret inputs, use approved tools, prepare structured outputs, and route exceptions for review. Benian can combine these capabilities, but customer-facing knowledge assistance is generally scoped as Chat AI while purpose-specific internal work belongs under AI Agents.

They can connect to agreed systems when the relevant API access, permissions, accounts, and integration support are available. Example scopes may include Slack, Linear, a CRM, or another work system. The engagement defines what information the agent can access, which actions it may prepare or perform, and who approves them. Integration availability, hosting, third-party tools, and ongoing support are specified in the proposal.

Benian establishes an evaluation set before launch, including ambiguous inputs, missing information, unsafe requests, and failed tool calls. The design can constrain tool access, require source support, expose relevant context, route uncertainty to a person, and log enough information for review. These controls reduce operational risk, but they do not guarantee perfect accuracy. Outputs should be evaluated against the agreed completion standard.

Timing depends on source access, integration complexity, and evaluation requirements, so the schedule is confirmed in the proposal after requirements, dependencies, acceptance criteria, and handover expectations are reviewed. Ongoing operation, expansion, maintenance, and support are scoped separately rather than assumed to be included in the initial build.

AI agent work is priced through a custom proposal for a defined agent and acceptance criteria. There is no public price. The proposal can distinguish implementation from model usage, third-party tools, hosting, and ongoing evaluation or support. Cost is shaped by the business job, approved sources, integrations, evaluation scope, access requirements, and operating responsibilities.

Benian designs systems to be delivered into customer-owned accounts and systems, with ownership and support responsibilities made explicit in the engagement. The client holds every login and key. The handover can include agreed code, configuration, operating instructions, and evaluation practices. Access permissions, hosting, credentials, model usage, maintenance, monitoring, and future changes should be documented so the customer understands how the system is operated.