Advisors

Agents are persistent AI specialists. Each has a distinctive robot name and glyph, a purpose, working instructions and a record of what it has learned. You can return to the same agent, choose its model, or bring a group into a conversation.

How agents arrive

You can start with an empty roster. When a task needs specialist expertise, Manwe looks for a suitable existing agent. If there is a gap, the selected model describes the expertise and working approach needed, and Manwe creates and keeps a new agent. Ordinary chat can proceed without creating one. Second opinions and deep reviews draw from the same roster and can add missing specialists.

You can also use New advisor to describe a role, working instructions and interests, or Import personas to bring descriptions from a document. Manwe assigns the robot identity; importing a perspective doesn't require a fictional human biography.

What makes each one different

The name and glyph stay with the agent. Its specialty, methods, relevant experience and own procedures shape how it approaches a task. A shared foundation covers clear answers, evidence and uncertainty; the agent's instructions add its particular way of working. These are AI specialists, not people with invented ages, careers or professional credentials.

When an agent answers, you can see whose contribution it is. Its identity and history survive closing and reopening Manwe. A model change changes the model doing the work, not which agent owns that history.

Find and call an agent

Open Advisors. Search or filter by specialty and source. Star useful agents, then press Shortlist to find them quickly. Open an agent's card to inspect its record. Talk to opens its saved one-to-one chat, or resumes that chat when you return. Follow-up messages stay with the same agent. The chat uses its assigned model, working instructions and relevant retained knowledge; you can stop a reply with the normal Stop control. New agent chats start without a project World selected and retain your existing personal-memory preference. Select Worlds in the composer when you want their context; that selection and the conversation history are saved. The agent header opens its card, and an assigned model label opens its model settings.

In a conversation, use the composer's Advisors or Invite control to choose participants. Start a message with @ and an agent's exact name to address it, or @Manwe to address Manwe. Follow-ups can stay with the same advisor. Smart chooses one invited advisor per turn; Panel asks the invited advisors in sequence. With multiple participants, Let them talk lets them discuss the question; Stop ends the work.

Reusable groups

Under Groups, press New group, give it a distinct name, describe its purpose and choose its members and coordinator. Select the team from the composer's @ menu or address its exact name. The coordinator assigns useful contributions, members work with their own models and normal tools, and the coordinator integrates and verifies the result. Members can leave scoped peer messages and the coordinator can assign follow-up work. Shared file changes run in sequence so one member can build on another's contribution. While a team is selected, the composer shows its effective assignments instead of asking you to choose one model for the whole team.

A team can work across your projects. Each run keeps its own project, assignments, dependencies, messages and results; each member keeps its exact model, connection and reasoning assignment and applicable memory scope. A missing model is reported. Completed work is preserved when another contribution fails. From an interrupted task’s details, Continue asks the saved coordinator to check the evidence and current state before assigning remaining work. Completed contributions stay intact, and uncertain changes are not replayed. A completed team run needs a new request. Second opinions remains a separate way to compare independent perspectives.

Choose each agent's model

Open its card and expand Agent settings. There are three choices:

Press Save agent settings to apply changes. If an assigned model or connection isn't available, Manwe reports it instead of silently choosing another provider. An agent's assignment also applies when it participates in a group or deep review.

Memory and experience

Agents keep relevant task experience, corrections, lessons and procedures. Private experience is scoped to its applicable project, team, Worlds and conversation; it doesn't become general knowledge for every chat or another agent. Recent learning shows the saved lessons, their scope and sources. Outcomes from check-ins also contribute to the agent's record.

This is separate from approved World memory. An agent learning a lesson does not approve a fact about you or promote a World memory card.

Background learning

While Manwe is open, online and has a usable model, eligible agents choose what would help them learn and when to return to it. They can research public sources, save lessons, develop their own procedures and propose better working instructions. Learning yields to work you request.

In an agent's settings, Learn independently controls participation, Daily learning tokens sets its maximum, and Learning Worlds limits its private learning context. With no Learning Worlds selected, learning uses public sources. Its Learning agenda shows the topic, reason, next attempt and any problem.

Defaults are 100,000 tokens per agent per day within a 1,000,000-token shared daily allowance. An active lesson can use up to 20% extra to finish and save what it learned, within the shared allowance. That extra allowance cannot start another learning job or continue researching. Input and output both count. Open Learning beside World/List in Advisors to change the shared allowance, pause learning, or see usage and the daily reset time. The button shows whether learning is on or paused. Choose Save to apply changes, or Cancel to discard them. Conversations and reviews you request use your provider as usual but do not spend this background allowance.

When a provider reports usage, Manwe uses it. Otherwise it keeps the reserved amount and marks usage as estimated. A request already underway can report more usage than expected, so this allowance is not a provider billing cap. Work that cannot fit waits for a later allowance; a saved instruction check can resume without repeating its completed baseline.

Improving instructions and procedures

Working instructions shows saved revisions and their checks. Background changes must improve on the existing instructions in the evaluation before becoming active. Restore previous version rolls an active change back. These checks cover particular tasks; they don't certify expertise or guarantee every answer.

Own procedures lists the agent's versioned ways of working. A newly written procedure is labelled untested; writing instructions is not proof they work. Learning changes saved context and methods, not the underlying model's weights.

Archive or start over

Archived keeps an agent's history but removes it from automatic selection and learning. Deleting one agent asks for confirmation with a button. Reset roster…, in the Advisors header’s options menu, shows what will be removed and asks for typed confirmation; it saves a recovery snapshot and preserves your Worlds, conversations, reports and user-owned skills. After a reset, new agents appear when tasks need them.