Agents that use tools, not just talk
An agent is a reusable assistant: a bound model, a defining persona, and a curated set of tools — all working together. Give it a goal and watch it reason, call the right tools, read the results, and deliver a complete answer.
The concept
What is an agent?
An agent is a three-part bundle: a provider and model, a system prompt that defines its persona and instructions, and a curated set of tools it is allowed to call. Once configured, it works autonomously — deciding when to reach for a tool and when to respond.
Bound model & persona
Each agent is locked to a specific provider and model — OpenAI, Anthropic, Gemini, Ollama, and more. A system prompt defines its role, tone, and constraints so it behaves consistently every time.
- Choose any connected provider and model
- Version-controlled system prompts
- Persona stays consistent across all conversations
Enabled tools
You decide which tools the agent may call — built-in utilities like web search, or your own HTTP endpoints. The agent only has access to the tools you explicitly enable, nothing else.
- Granular per-agent tool permissions
- Mix built-in and dynamic HTTP tools
- Every tool call is visible in the thread
Autonomous tool-calling
The model decides when a tool is needed, constructs the call, reads the result, and continues reasoning — all without human intervention. Every step streams live in the chat thread.
- Zero-shot tool selection by the model
- Multi-step tool chains in a single turn
- Full call-and-result trail visible inline
Step by step
How an agent works
From the moment you send a message to the final streamed response, here is what happens inside every agent turn.
User sends a prompt
The conversation turn is forwarded to the bound model with its full history and system prompt.
Model reasons
The model reads the system prompt and conversation history, then decides what to do next — answer directly or call a tool.
Tool call issued
When more information is needed, the model emits a structured tool-call (e.g. web_search or http_request).
Result returned
The platform executes the tool, captures the result, and streams it back into the model's context — all visible in the thread.
Model responds
With full context including tool results, the model produces its final answer — streamed live, token by token.
Use cases
What can you build?
Agents are general-purpose — here are a few of the most common starting points, each deployable in minutes.
Research assistant
Give the agent web search and URL-reading tools. Ask a question and it scouts multiple sources, cross-references them, and returns a cited markdown report — in a single turn.
- Automatic multi-source cross-referencing
- Inline citations in the response
- Configurable search depth and source limit
Support triage
Bind the agent to your knowledge-base API and ticket system. It classifies incoming requests, fetches the relevant docs, drafts a reply, and escalates when confidence is low.
- Classifies and routes issues automatically
- Pulls from live knowledge-base via HTTP tool
- Escalation trigger on low-confidence answers
Data lookup & internal API
Expose your internal REST API as an HTTP tool. The agent queries it on demand — fetching records, aggregating results, and formatting them for the user — all inside the chat.
- No custom integration code required
- Auth headers stored securely per tool
- Structured results rendered as tables
Content drafting
Pair a writing-focused system prompt with a web search tool. The agent researches the topic, outlines the piece, and drafts each section — ready for your review and iteration.
- Research-then-write loop in one agent
- Consistent tone enforced via system prompt
- Iterative revision supported in the same thread
Agent builder
Build an agent in minutes
Open the agent builder inside the app, give it a name, pick your provider and model, write a system prompt, and select the tools it may use. Hit save — your agent is live instantly, no deployment required.
Share agents with your team
Once an agent is saved you can publish it to your team workspace. Every member can use it in their own chats and workflows — and you control who can edit the definition. Learn about team collaboration →Related
Start building with The Chat Agent
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