Configure agents so roles and costs stay clear
Agents in QuantenRam only create real value when roles, models, and handoffs work together cleanly. It's not the name of a persona that matters, but whether planning, implementation, and review run in a reproducible sequence and use the appropriate alias tier at each step.
The most practical configuration starts with a few clear roles. A Planner or Prometheus-like agent assigns tasks and stays cost-conscious on a Start model. An implementation role like Hephaestus or Sisyphus gets the fast coding path. A review role like Oracle or Momus isn't the default for everything, but is activated only for architecture, risk, or final approval. This makes multi-agent work traceable and billing escalation targeted rather than random.
Planning
Planning roles gather context, break down tasks, and define the verify loop. In QuantenRam, they should stay on quantenram-start/* by default, as structural clarity is usually more important than maximum model strength.
Implementation
Implementation roles work on quantenram-coding/* when diff analysis, refactoring, and tool-assisted loops are the focus. This lane is usually the most economical and operationally cleanest choice for longer coding sessions.
Review
Review roles should be bound to clear triggers, such as architecture decisions, security-relevant changes, or final risk assessments. This is where quantenram-zenmaster/* pays off, while routine checks are better kept in cheaper lanes.
Configure roles and responsibilities
A good agent configuration assigns three things to each role: a primary model, optionally a fallback, and an escalation condition. This makes it clear before the first prompt why a role exists and when it should be used. For teams, this is valuable because it creates a repeatable operational contract rather than a personal preference.
{
"agents": {
"prometheus": {
"model": "quantenram-start/glm-5",
"fallback_model": "quantenram-start/deepseek-chat"
},
"hephaestus": {
"model": "quantenram-coding/qwen3codernext",
"fallback_model": "quantenram-coding/qwen3.5-9b"
},
"oracle": {
"model": "quantenram-zenmaster/gpt-5.4",
"trigger": "architecture-or-risk"
}
}
}The crucial aspect of this configuration isn't the exact syntax, but the operational logic behind it. The planner stays cheap and fast, the implementation role gets the coding-oriented path, and the review role has a clear, rare trigger. This is what good multi-agent setups look like in QuantenRam.
Multi-agent workflows without context loss
Multiple agents only help when handoffs are clean. Each role should always hand over with a concrete artifact: a plan, a diff boundary, a test status, or an open risk question. Simply "handing over" without sending along the goal and verification status produces double research and makes later review loops unnecessarily expensive.
// Handoff structure between agents
{
"handoff": {
"from": "prometheus",
"to": "hephaestus",
"artifact": {
"plan": "Refactor auth module into service layer",
"test_requirements": ["All existing tests pass", "New tests for service layer"],
"risk_flags": ["Backward compatibility with old API"],
"max_budget": "$0.50"
}
}
}This structure ensures that the receiving agent has all the context needed to continue without recreating it. The artifact becomes the contract between roles, making the workflow reproducible and auditable.
Agent configuration is an investment in operational clarity. When roles are well-defined and handoffs are structured, multi-agent workflows become a force multiplier rather than a coordination nightmare.