AI agent

What separates an AI agent from a chatbot?

A chatbot uses scripted dialogue flows or a single LLM call to respond. It is reactive and limited to text generation. An AI agent, by contrast, is allowed to take actions: query a CRM, schedule a meeting, refund an order, send an email. The agent decides which tools to call, in which order, and what to do with the results, all without a human writing the flow in advance.

Three concrete differences:

• Tool use: an agent can call functions; a chatbot only generates text.
• Multi-step reasoning: an agent can chain actions (look up the order, check shipping status, draft a refund) within one user request.
• State management: an agent persists context across turns and across tool calls within a session.

How AI agents are built

An AI agent is composed of four primary parts:

1. A foundation LLM (Anthropic Claude, OpenAI GPT, Mistral, etc.) that handles language understanding and decision-making.
2. A toolset, typed function definitions the LLM is allowed to call (e.g. `get_order_by_id`, `send_email`).
3. An orchestrator that mediates between the LLM and the tools, executes tool calls, and feeds results back into the conversation.
4. Memory / context layer that retains prior turns, retrieved knowledge, and tool outputs across the session.

Modern agent platforms, including Conveya, use the LLM provider's native tool-calling protocol (e.g. Anthropic's tool_use blocks or OpenAI's function calling) so the orchestration logic stays declarative.

When to use an AI agent vs a chatbot

AI agents are appropriate when the answer requires data that is not in the chatbot's training set, order status, customer records, calendar availability, and when the user expects an action, not just an answer. Use a chatbot only for fixed-response Q&A where every possible answer can be authored in advance.

In customer support specifically, the cost-per-resolution of an agent is typically lower than a chatbot at any non-trivial scale, because agents resolve cases end-to-end instead of escalating to humans for data lookups.

Risks and limitations

• Hallucination: when an agent fabricates a tool call or a fact. Mitigated by typed tool definitions and result validation.
• Action authorization: an agent can in principle take destructive actions (delete data, send mass emails). Mitigated by per-tool permission scopes and human-in-the-loop gates for high-risk operations.
• Latency: tool calls add round-trips. A well-engineered agent stays under three seconds per turn for support use cases.
• Cost: agents consume more LLM tokens than single-call chatbots. Mitigated by aggressive caching and small-model routing for simple turns.