Agent Orchestration

Coordinate multi-agent workflows with reliable, observable task execution

Multi-agent systems fail in ways single agents do not: partial progress gets lost, agents deadlock waiting for each other, and debugging distributed failures is painful. We provide orchestration infrastructure that makes multi-agent workflows reliable and transparent by default.

Orchestration Patterns#

Supervisor-Worker#

A planner agent breaks a task into subtasks and dispatches them to specialized workers. Results are collected and synthesized.

When to use: Complex tasks that benefit from specialization — research + writing + review, code generation + testing + documentation.

What we manage:

• Subtask queue with priority and dependency ordering
• Worker assignment based on capability and availability
• Result aggregation with partial-failure handling
• Supervisor context injection and handoff protocol

Parallel Pipelines#

Multiple agents run simultaneously on independent subtasks. Results merge at a synchronization point.

When to use: Tasks with clear parallel branches — scraping multiple sources, processing dataset chunks, running independent analyses.

Features:

• Configurable concurrency limits per pipeline
• Fan-out / fan-in with configurable merge strategies
• Early termination if a critical branch fails
• Per-branch timeout independent from total pipeline timeout

Sequential Chains#

Agents execute in series, each receiving structured output from the previous step.

When to use: Step-by-step pipelines where each stage depends on the previous — data extraction → enrichment → classification → storage.

Features:

• Typed handoff schemas enforced at each step boundary
• Conditional branching based on intermediate results
• Automatic step retry on transient failure

Human-in-the-Loop#

Pause workflow execution at defined checkpoints for human review or approval.

Integration options:

• Webhook callback when approval is needed
• Slack notification with approve/reject actions
• Email with secure one-click response
• Custom UI embed via provided React component

Durable Execution#

Agent orchestration fails without durability. Every workflow step is:

1. Checkpointed — state written before and after execution
2. Idempotent — safe to re-run after a crash without duplicate side effects
3. Retryable — configurable retry policy with backoff and jitter
4. Observable — execution DAG visible in real time in the dashboard

Failure Handling#

Workflow Definition#

Define workflows in code or via our YAML schema — no visual-only tools that become bottlenecks.

TypeScript SDK:

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import { Workflow, agent, parallel } from '@assistance/agents'
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const research = agent('researcher')
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const writer = agent('writer')
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const reviewer = agent('reviewer')
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export const reportWorkflow = new Workflow({
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  steps: [
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    parallel([research('market-trends'), research('competitor-analysis')]),
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    writer.synthesize(),
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    reviewer.approve({ minScore: 0.8 }),
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  ],
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  timeout: '15m',
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  retries: { max: 3, backoff: 'exponential' },
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})

YAML definition also supported for teams preferring configuration-as-code without a build step.

Scheduling#

• Cron schedules — run workflows on a recurring schedule
• Event triggers — start on webhook, queue message, or database change
• Chained triggers — one workflow's completion triggers another
• Manual dispatch — API or dashboard one-click run with optional input override

Getting Started#

Related Services#

• AI Agent Infrastructure
• Agent Observability
• Managed MCP Servers