agentica-spawn

• After agentica-orchestrator prompts user for pattern selection
• When user explicitly requests a multi-agent pattern (swarm, hierarchical, etc.)
• When implementing complex tasks that benefit from parallel agent execution
• For research tasks requiring multiple perspectives (use Swarm)
• For implementation tasks requiring coordination (use Hierarchical)
• For iterative refinement (use Generator/Critic)
• For high-stakes validation (use Jury)

Swarm (Research/Explore)

```python

swarm = Swarm(

perspectives=[

"Security expert analyzing for vulnerabilities",

"Performance expert optimizing for speed",

"Architecture expert reviewing design"

],

aggregate_mode=AggregateMode.MERGE,

)

result = await swarm.execute(task_description)

```

Hierarchical (Build/Implement)

```python

hierarchical = Hierarchical(

coordinator_premise="You break tasks into subtasks",

specialist_premises={

"planner": "You create implementation plans",

"implementer": "You write code",

"reviewer": "You review code for issues"

},

)

result = await hierarchical.execute(task_description)

```

Generator/Critic (Iterate/Refine)

```python

gc = GeneratorCritic(

generator_premise="You generate solutions",

critic_premise="You critique and suggest improvements",

max_rounds=3,

)

result = await gc.run(task_description)

```

Jury (Validate/Verify)

```python

jury = Jury(

num_jurors=5,

consensus_mode=ConsensusMode.MAJORITY,

premise="You evaluate the solution"

)

verdict = await jury.decide(bool, question)

```

All spawned agents receive:

• SWARM_ID: Unique identifier for this swarm run
• AGENT_ROLE: Role within the pattern (coordinator, specialist, etc.)
• PATTERN_TYPE: Which pattern is running