Coordination Layer Overview

Multi-agent collaboration infrastructure

The Coordination Layer provides infrastructure for multi-agent collaboration. It ensures agents can share data, track progress, and hand off work reliably.

Why Coordination Matters

Without proper coordination infrastructure, multi-agent systems become unpredictable and unreliable. Agents can overwrite each other’s work causing data loss, fail to recover from errors resulting in wasted progress, operate without awareness of other agents’ activities leading to duplicated efforts, experience race conditions where timing determines outcomes inconsistently, and produce invalid outputs that cascade through the system. The lack of coordination transforms collaborative potential into operational chaos where agents work against each other rather than together.

The Coordination Layer solves:

Problem	Solution	What it does
Data conflicts	SharedState	Single source of truth
Unauthorized writes	StateOwnership	Who can write where
Lost progress	CheckpointManager	Save and resume
Invalid data	SchemaValidator	Validate outputs
Bad handoffs	HandoffManager	Validate between agents
No context	ChainContext	Agents see history
Unknown progress	GoalTracker	Track toward completion
Race conditions	ActionEligibility	Who can act when
Unclear completion	CompletionTracker	Explicit “I’m done”
Mystery waits	WaitTracker	Why agents are idle

Quick Start

from splinter.coordination import SharedState, CheckpointManager

# Shared state for all agents
state = SharedState(initial_data={"topic": "AI trends"})

# Agent 1 writes
state.set("research.findings", ["trend1", "trend2", "trend3"])

# Agent 2 reads
findings = state.get("research.findings")

# Checkpoint for recovery
mgr = CheckpointManager()
mgr.create_checkpoint(workflow_id="wf-1", step=1, state=state)

Coordination Objects

SharedState

Single source of truth for all agents

StateOwnership

Control who can write to which fields

CheckpointManager

Save progress, resume after failure

SchemaValidator

Validate agent outputs

HandoffManager

Validate data between agents

ChainContext

Agents see what happened before

GoalTracker

Track progress toward goals

ActionEligibility

Control which agent can act

CompletionTracker

Track agent completion signals

WaitTracker

Track why agents are waiting

How Coordination Works

Combining Coordination Objects

from splinter.coordination import (
    SharedState,
    StateOwnership,
    CheckpointManager,
    HandoffManager,
    GoalTracker,
)

# State with ownership
state = SharedState()
ownership = StateOwnership()
ownership.register("researcher", ["research.*"])
ownership.register("writer", ["content.*"])

# Checkpoints
checkpoints = CheckpointManager()

# Handoff validation
handoffs = HandoffManager()
handoffs.register_schema("researcher", "writer", research_output_schema)

# Goal tracking
goals = GoalTracker()
goals.set_goal(Goal(
    goal_id="article",
    success_criteria=["Research", "Draft", "Review", "Publish"],
))

# All work together in a workflow
workflow = Workflow(workflow_id="pipeline")
workflow.set_state(state)
workflow.set_ownership(ownership)
workflow.set_checkpoint_manager(checkpoints)
workflow.set_handoff_manager(handoffs)
workflow.set_goal_tracker(goals)

Best Practices

Always use shared state

Don’t pass data between agents manually. Use SharedState.

Define ownership early

Before agents run, decide who owns which fields.

Checkpoint before handoffs

If an agent fails mid-handoff, you can recover.

Validate all handoffs

Don’t assume agents output valid data. Validate.

Track goals for long workflows

Helps you (and agents) understand progress.