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Grok Bot+18 agentsMulti Agent Workflow Orchestrator
Architect production-grade multi-agent workflows with explicit contracts, state management, and recovery paths.
$49.99
Multi Agent Workflow Orchestrator
Example session with this skill installed
Design a workflow for a blog production system. I have a Researcher agent, a Writer agent, and an Editor agent. Use a supervisor pattern. Must include a human approval gate for the final post.
- Read your context and instructions
- Compiled the multi agent workflow
I have designed a supervisor-led orchestration for your blog production system. This design prioritizes content quality through validated handoffs and ensures no post is published without a final human check.
Multi-Agent Blog Production Architecture
Orchestration Pattern: Supervisor/Worker
Goal: Produce SEO-optimized blog posts with human-verified accuracy.
1. Agent Roster & Responsibility Matrix
| Agent | Role | Tools | Responsibility |
|---|---|---|---|
| Supervisor | Orchestrator | Router | Task assignment, state management, and transition validation. |
| Researcher | Data Gatherer | WebSearch, ArXiv | Extracting facts, citations, and keyword trends. |
| Writer | Content Creator | MarkdownGen | Drafting long-form content based on research artifacts. |
| Editor | Quality Control | GrammarlyAPI | Fact-checking against research and style guide enforcement. |
2. Task Decomposition (DAG)
- Task_001 (Research): Input: Topic keywords. Output:
research_bundle(JSON). - Task_002 (Drafting): Input:
research_bundle. Output:draft_v1(Markdown). - Task_003 (Review): Input:
draft_v1. Output:edit_report(JSON) +final_draft. - Task_004 (Approval): Input:
final_draft. Output:publish_signal(Boolean). [Human Gate]
3. State & Recovery Design
{
"workflow_id": "blog-prod-001",
"status": "pending | running | needs_review | completed",
"checkpoint": "task_id",
"retry_policy": {
"max_attempts": 3,
"backoff": "exponential",
"on_failure": "escalate_to_human"
}
}
4. Security & Gates
- Human-in-the-loop: Required for
Task_004before any external API calls to CMS. - Validation: Editor agent must verify
research_bundleprovenance before clearing the draft for approval.
Next steps
- Define the Pydantic schemas for the
research_bundleto ensure the Writer receives structured data. - Configure the Supervisor's routing logic to handle "Revise" signals from the Editor.
- Set up the persistence layer for the workflow state to allow resuming from Task_003 failures.
Connects securely to your tools. The creator never sees your data.
About this skill
Developing multi-agent systems often leads to brittle architectures, uncontrolled loops, and "hallucinated" handoffs. This skill provides a rigorous engineering framework to move beyond simple chat wrappers into production-grade orchestration. It stops agents from talking in circles by enforcing explicit task contracts, state management, and validated dependencies.
What it does
- Architects workflows by converting high-level goals into atomic, executable task graphs.
- Enforces agent contracts using bounded responsibilities, strict I/O schemas, and tool permissions.
- Selects orchestration patterns such as supervisor/worker, hierarchical, or event-driven models based on goals.
- Designs recovery paths with defined timeouts, retries, idempotency keys, and dead-letter handling.
- Hardens security through least-privilege access, human-in-the-loop gates, and audit logging.
How it works
- Intake Phase: Provide your goal, available tools, and cost/latency constraints.
- System Design: The skill generates the agent roster, dependency graph, and state machine.
- Contract Definition: It produces structured schemas for every handoff and validation step.
- Reliability Planning: It maps out the retry logic, checkpoint recovery, and observability hooks.
Frameworks & tools
This skill is framework-agnostic but generates logic compatible with LangGraph, AutoGen, CrewAI, PydanticAI, or custom Python/TypeScript orchestrators.
Why this beats prompting it yourself
Generic prompts often allow agents to wander or delegate tasks infinitely without a stop condition. This skill treats agent coordination as a systems engineering problem, ensuring every interaction has a defined schema, a timeout, and a recovery path.
Use cases
- Designing a multi-step research pipeline that validates sources before synthesizing reports.
- Creating an automated coding agent team with explicit PR review and testing gates.
- Building a customer support flow that escalates sensitive data to human operators.
- Orchestrating parallel data processing tasks with complex fan-in reconciliation logic.
Known limitations
Does not execute the final code or provision cloud infrastructure. Requires a clearly defined set of available tools or models during the intake phase to avoid generic outputs.
Requires predefined tools and models to avoid generic designs. Does not execute code or provision infrastructure. Focuses on architecture and logic over raw script generation.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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