Compose Orchestration

Define multi-agent DAG workflows in YAML. The Compose engine handles dependency resolution, parallel scheduling, result passing, token budget pools, and SLA contracts.

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Quick Start

# compose.yaml
version: "1.0"
intent: "Code review workflow"
model: "deepseek-v4-flash"

agents:
  analyzer:
    intent: "Analyze code quality of kernel/kernel.go"
    agent: "code-analyst"
  doc-gen:
    intent: "Generate improvement documentation"
    depends_on:
      analyzer: completed
  checker:
    intent: "Verify analysis quality"
    depends_on:
      doc-gen: completed

$ rnix compose up
compose | Code review workflow | starting
[layer 1/3] analyzer   PID 5 | completed | 3.8s | 1,450 tokens ✓
[layer 2/3] doc-gen    PID 6 | completed | 4.2s | 1,180 tokens ✓
[layer 3/3] checker    PID 7 | completed | 2.5s | 890 tokens ✓
compose | completed | 3/3 agents | 10.5s | 3,520 tokens

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DAG Scheduling

The engine automatically:

1. Parses dependency graph — builds DAG from depends_on
2. Topological sort — determines execution layers
3. Parallel execution — agents in the same layer run concurrently
4. Result injection — upstream output injected into downstream context

analyzer ──→ doc-gen ──→ checker      # Linear
    A ←─ C ─→ B                       # C blocks both; A, B parallel after C

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Agent Fields

Field	Type	Description
intent	string	Task description
agent	string	Named agent definition (optional)
model	string	Model override
provider	string	Provider override
depends_on	map	<upstream>: completed
timeout	duration	Execution timeout
max_retries	int	Retry on failure
budget	int	Token budget (drawn from pool)
priority	string	high / normal / low

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Token Budget Pools

Assign a shared token budget across the workflow:

budget_pool:
  total: 50000
  allocation: priority    # priority | equal | proportional

agents:
  critical-agent:
    budget: 20000
    priority: high
  helper-agent:
    budget: 5000
    priority: low

When agents compete for limited budget, high-priority agents get preference. See Token Economy.

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SLA Contracts

Define quality expectations between agents:

agents:
  analyzer:
    intent: "Analyze code"
    contract:
      output_quality: "Must contain at least 3 actionable items"
      max_tokens: 3000
      timeout: 30s
      sla_level: gold

Post-execution SLA evaluation feeds into the Reputation System.

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Result Injection

Upstream agent output is automatically injected into downstream agent context. When agent B depends on agent A, A's final result is prepended to B's system prompt as a tool message:

[Upstream Result from analyzer]
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<analyzer's output here>
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This allows downstream agents to build on upstream results without explicit plumbing.

Error Handling

Scenario	Behavior
Agent fails (exit code 1)	Downstream agents are cancelled, workflow marked as failed
Agent timeout	Configured timeout exceeded → agent killed with SIGTERM
Agent exceeds budget	Exit code 2, treated same as failure
All retries exhausted	Agent marked permanently failed, downstream cancelled

Set max_retries to automatically retry failed agents:

agents:
  flaky-analyzer:
    intent: "Analyze code"
    max_retries: 3    # retry up to 3 times on failure

Compose File Reference

Field	Type	Default	Description
version	string	—	Compose spec version ("1.0")
intent	string	—	Workflow description (shown in CLI output)
model	string	—	Default model for all agents
budget_pool	object	—	Shared token budget configuration
agents	map	—	Agent definitions (key = agent name)

Budget Pool Fields

Field	Type	Default	Description
total	int	—	Total token budget across all agents
allocation	string	priority	Allocation strategy: priority, equal, or proportional

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Commands

rnix compose up              # Start workflow
rnix compose up --json       # JSON output
rnix compose down            # Stop all agents and cleanup

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Related Documentation

• AgentShell — Scripting-based orchestration
• Intent System — Declarative intent decomposition
• Token Economy — Budget pools and reputation
• Monitoring — rnix top for real-time process view