From model to agent
A model generates an answer from its input. An agent adds a persistent identity, instructions, context, tools and a defined responsibility. Galaris connects that working role to its missions and outputs so the setup can be reused.
Configure a working role
Give the agent a name, purpose and instructions. Select its inference settings and Task harness, then assign reusable skills and permitted connections. Conversation uses the internal controller and the Low text tier; the selected harness is used only when a Task runs.
Build the team around a real need
A coordinator can clarify the request and consolidate results. Add a specialist when it brings a different skill, Task harness or access boundary. The user retains the decisions that require human judgment; Galaris preserves the relationships between tasks, waits and deliverables.
Capabilities have boundaries
An agent does not automatically receive every tool, every conversation or unrestricted authority. The effective tool catalogue is filtered before execution. Durable memory follows its own access rules. Check sources and important effects: configuration does not make model output infallible.
From instructions to working resources
A model alone does not define an agent’s capabilities. A note needs accessible sources and a document destination. An interactive website requires a browser connection. Email needs a mailbox connection and its approval policy.
Start with a clear role, then add useful tools and skills. The agent keeps its identity when its model profile or Task harness changes.
Where should you start?
Start with a conversation for a one-off question. Prepare a mission when you need a deliverable with defined steps and access. A multi-agent system adds a division of responsibilities: each contribution should have a purpose you can verify.
The AI agent or chatbot guide helps you choose based on the work. Then build an AI agent team and examine its memory and MCP tools.