Agents that reason, act, and collaborate.
The AI Agent node is a true reasoning runtime. Multi-agent teams share memory, debate, and ship — model-agnostic, tool-equipped, and MCP-ready.
What agents take off your plate.
Reasoning runtime
Agents plan, call tools, and adapt — not single-shot prompts.
Multi-agent teams
Specialized roles share memory and hand off work.
MCP-ready
Connect any MCP server to give agents new tools in minutes.
Model-agnostic
Pick the best model per task; swap without re-platforming.
Tool use
Web, CMS, CRM, warehouse — agents act on real systems.
Memory
Grounded in your knowledge base, brand voice, and past work.
A reasoning runtime, not a prompt box.
Give the agent a goal and it plans the steps, calls tools, observes the results, and adapts until it's done — bounded by the tool-call limit and instructions you set.
Tools, datasources, and MCP — wired in.
Equip an agent with exactly the abilities a task needs. It calls them on its own as it reasons.
Pick the right brain for each agent.
Choose a reasoning-capable model per node — GPT-4, Claude Sonnet/Opus, or Gemini Pro — and tune temperature, top-K, top-P, and max tokens. Swap models without re-platforming.
How an agent comes together.
Define the goal
Drop an AI Agent node into Studio, write the system prompt, and set the tool-call limit — no code required.
Attach capabilities
Equip the agent with tools, MCP servers, and RAG datasources so it can act on real systems.
Run and observe
Fire the workflow — watch the agent plan, call tools, and adapt step by step in the execution log.
Review and refine
Inspect every reasoning step and tool call, tune the prompt or tools, and iterate until it ships reliably.
What teams ask before they commit.
What is an AI agent in Draft & Goal?
In Draft & Goal, an AI agent is a reasoning runtime, not a single-shot prompt. You give it a goal and it plans the steps, calls tools, observes the results, and adapts until the task is done — bounded by the tool-call limit and the instructions you set.
Which AI models can Draft & Goal agents run on?
Draft & Goal is model-agnostic. You pick a reasoning-capable model for each agent node — GPT-4, Claude Sonnet or Opus, or Gemini Pro — and tune temperature, top-K, top-P, and max tokens. Because the choice is made per node, teams can swap models without re-platforming their workflows.
What is a multi-agent team?
A multi-agent team is a group of specialized agents that share memory and hand off work to each other — for example a researcher, a writer, and a reviewer collaborating on one deliverable. Draft & Goal coordinates these roles so each agent handles the part of the process it is built for.
What tools and data can an agent use?
You equip each agent with exactly the capabilities its task needs: web search, a web scraper, API connectors, RAG datasources, MCP servers, and structured JSON output. The agent calls them on its own as it reasons, acting on real systems such as your CMS, CRM, or data warehouse.
Do I need to code to build an agent in Draft & Goal?
No. You drop an AI Agent node into Studio, write the system prompt, and set the tool-call limit — no code required. You can then attach tools, MCP servers, and RAG datasources, run the workflow, and inspect every reasoning step and tool call in the execution log.
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