Complete documentation of how AI agents interact with Marcus throughout their lifecycle.
Understand the sophisticated multi-system orchestration behind every agent action. These guides reveal the intelligence, coordination, and learning happening when agents work with Marcus.
- AI agents using Marcus for coordination
- Developers building agent integrations
- System architects understanding agent coordination
- Anyone debugging agent behavior
Complete agent lifecycle from startup to continuous work loop. Understand the decision process, tool usage, and coordination patterns.
When to read: First document for understanding agent operations
How agents register with Marcus and become part of the coordination system. Covers profile creation, capability evaluation, and team integration.
Systems involved: Agent Management, Event System, AI Decision Engine, Memory System (5+ stages)
The sophisticated 8-stage process of task assignment. From project state refresh to AI-powered task selection to context building and instruction generation.
Systems involved: Agent Coordination, Project Management, AI Engine, Context System, Lease Management, Memory (15+ systems)
What happens when agents report progress at 25%, 50%, 75%, or 100%. Covers lease renewal, performance learning, predictive analytics, and cascade coordination.
Systems involved: Lease Management, Kanban Integration, Memory, Predictive Analytics, Monitoring (7+ stages)
AI-powered blocker analysis and resolution. Learn how Marcus analyzes root causes, generates solutions, assesses risk, and coordinates team response.
Systems involved: AI Blocker Analysis, Risk Assessment, Memory, Task Management, Communication Hub (7+ stages)
How agents retrieve comprehensive task context including dependencies, implementation patterns, architectural decisions, and risk assessment.
Systems involved: Core Models, Kanban Integration, Context System, Code Analysis, Memory, Risk Analysis (5+ stages)
Sophisticated dependency validation with graph analysis, status checking, predictive risk analysis, and optimization recommendations.
Systems involved: Context Analysis, Dependency Engine, Predictor Engine, Coordination Hub, Optimizer Engine, Learning (7+ stages)
These aren't simple API docs—they reveal the internal complexity and intelligence behind each operation:
- Multi-stage orchestration - 4-8 stages per operation
- Multi-system integration - 8-15+ systems working together
- AI-powered intelligence - Extensive AI analysis, prediction, optimization
- 4-tier memory integration - Continuous learning at every step
- Predictive analytics - Risk assessment, timeline forecasting, impact analysis
- Comprehensive logging - Complete audit trail and observability
All agents follow this continuous loop:
1. Register (once) → 2. Request Task → 3. Get Context (if needed) →
4. Work on Task → 5. Report Progress (25%, 50%, 75%) →
6. Report Completion (100%) → 7. IMMEDIATELY Request Next Task → (loop to step 2)
Critical behaviors:
- Complete tasks before requesting new ones
- Request next task IMMEDIATELY after completion
- Log decisions AS they're made
- Report blockers with attempted solutions
- Use context to understand dependencies
- New to Marcus? Start with Agent Workflow Overview
- Building an agent? Read all guides in order
- Debugging? Find the relevant workflow guide
- Need API details? See API Reference
Remember: These operations look simple from outside, but they orchestrate sophisticated intelligence for effective coordination.