agenthub

Agent Building

Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work.

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AgentHub - Multi-Agent DAG Orchestration

AgentHub provides patterns and tools for orchestrating multiple AI agents as a directed acyclic graph (DAG). Instead of one agent doing everything sequentially, AgentHub lets you decompose complex tasks into sub-tasks, assign each to a specialized agent, define dependencies between them, and merge their outputs into a coherent result.

The core insight: complex tasks decompose better than they scale. A 10-step sequential task run by one agent hits context limits and quality degradation. Five parallel agents with clear scopes and a merge step produce better results faster.

Core Capabilities

DAG workflow design — model tasks as nodes with explicit input/output contracts and dependency edges.
Parallel execution — topological sort, parallel groups, and max_parallel scheduling for real speedup.
Agent lifecycle — spawn, monitor (board), and track states from PENDING through COMPLETED/FAILED.
Quality gates — evaluate outputs against thresholds and rank competing results.
Output merging — synthesize, rank-select, or chain terminal outputs into a coherent deliverable.

When to Use

A task needs multiple specialized agents with distinct scopes.
You want to parallelize AI work that would otherwise run sequentially.
A single agent hits context limits or quality degradation on a long task.
You need quality gates and merge strategies across agent outputs.

Clarify First

Before designing the workflow, confirm these inputs. If any is unknown or vague, ASK — do not assume:

Task decomposition — how the work splits into agent sub-tasks and their dependencies (defines the DAG nodes and edges in Init)
Parallelism budget — how many agents may run concurrently (sets max_parallel scheduling)
Merge strategy — synthesize, rank-select, or chain (determines how the Merge stage combines outputs)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Sub-Skills

This skill uses compound sub-skill architecture. Each sub-skill in skills/ handles a stage of the orchestration lifecycle:

Sub-Skill	File	Purpose
Init	`skills/init.md`	Initialize a multi-agent workflow definition
Run	`skills/run.md`	Execute a defined workflow end-to-end
Spawn	`skills/spawn.md`	Spawn individual agents within a workflow
Board	`skills/board.md`	Dashboard showing agent status and progress
Eval	`skills/eval.md`	Evaluate agent outputs for quality and consistency
Merge	`skills/merge.md`	Merge outputs from multiple agents into final result
Status	`skills/status.md`	Show workflow execution status and health

Lifecycle: Init defines the workflow DAG, Run orchestrates execution, Spawn creates individual agents, Board provides real-time visibility, Eval checks output quality, Merge combines results, and Status reports overall health (Init → Run → Spawn (parallel) → Eval → Merge, with Board/Status reading state throughout).

Tools

Tool	Purpose	Command
`dag_analyzer.py`	Validate DAG definitions (cycles, unreachable nodes, critical path)	`python scripts/dag_analyzer.py --workflow workflow.json --validate --critical-path`
`session_manager.py`	Manage orchestration sessions and state	`python scripts/session_manager.py create --json`
`board_manager.py`	Manage agent task boards with status tracking	`python scripts/board_manager.py --session session.json --view board`
`result_ranker.py`	Rank and merge outputs from multiple agents	`python scripts/result_ranker.py --session session.json --rank --merge synthesize`

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

references/orchestration-core.md — workflow DAG concepts, the full workflow-definition JSON format, agent states, execution strategy, the define/execute/evaluate workflows, and the common DAG patterns (fan-out/fan-in, pipeline, reducer, validator chain). Read when designing or running a workflow.
references/multi-agent-patterns.md — the deep pattern catalog (fan-out/fan-in, pipeline, reducer, validator chain, map-reduce, diamond dependency), agent design principles, quality-gate patterns, failure handling, scaling table, and metrics targets. Read when choosing a pattern or designing quality gates and failure handling.
references/operations-and-quality.md — best practices, common pitfalls, troubleshooting table, and success criteria. Read when debugging a workflow or validating it against the quality bar.

Scope and Limitations

This skill covers:

Multi-agent workflow design with DAG dependency graphs
Agent spawning, monitoring, and lifecycle management
Output quality evaluation and ranking
Result merging strategies for coherent final deliverables

This skill does NOT cover:

Individual agent design or prompt engineering (see agent-designer)
Agent memory and self-improvement (see self-improving-agent)
Infrastructure for running agents (compute, scheduling, deployment)
Real-time streaming communication between agents

Integration Points

Skill	Integration	Data Flow
`agent-designer`	Defines individual agent capabilities that become DAG nodes	Agent specs flow in; execution results flow back for agent tuning
`self-improving-agent`	Each agent can use self-improvement patterns to get better	Session feedback from orchestration feeds into agent learning loops
`prompt-engineer-toolkit`	Agent task prompts benefit from prompt engineering	Optimized prompts improve individual agent quality within the DAG
`context-engine`	Manages what context each agent sees	Context retrieval provides relevant inputs to each spawned agent
`observability-designer`	Monitors workflow execution and agent health	Agent state transitions and timing metrics feed into dashboards