AI tools are doing everything with the help of AI agents. One AI agent is enough to complete multiple tasks. But sometimes, you need multiple agents to complete the complex tasks. This is where engineers get stuck.
The biggest issues with multi-agents are that they often fail or fail to collaborate. The most common AI agents’ failure includes missing agentic structure. You need to understand the 3 different engineering patterns that make AI agents reliable.
Developers working on multi-agent workflows often see failures.
Here is everything about Multi-Agent Workflow, how it works, and why it fails.
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Multi-Agent Workflow Failure:
AI agents complete tasks based on the established system.
During this process, most of the failures occur.
For example, one agent complete task while the other is still working on it. This causes the collaboration issue. It can fail any downstream check.
The issue occurs when agents handle related tasks, such as:
- Training issues
- Run checks
- Propose changes
- Open Pull Request
During these tasks, AI agents make assumptions about:
- State of request
- Order
- Validation
AI agents fail when they lack interfaces, instructions, and data formats.
AI agents handle the agentic experiences during multi-agent orchestration patterns and internal automation through GitHub Copilot. The role of an AI agent is to work as a distributed system, not as a chat interface.
Multi-Agent System:
A multi-agent system is required to complete a complex workflow. Introducing multi-agents also leads to an increasing number of agentic failures.
Developers use a multi-agent workflow:
- Codebase Maintenance
- Dependency Updates
- Automated code quality checks
- Refactors
- Feature implementation
- Pull requests and Issues.
These work when you work with constrained and explicit steps.
Here are the most common multi-Agent failure and fixes:
Messy Natural Language:
LLMs allow users to use natural language. Processing natural language in AI agentic language is itself a task. Multi-Agent workflow fails when multiple agents fail to exchange misleading or confused language. Inconsistent JSON also causes the issue.
When a team build AI agent, it creates contracting the early stage for easy understanding. AI agents also require clear data usage policies.
AI structure requires a strict schema and typed interfaces. Machines check data sent by AI agents. Wrong data immediately fails. This prevents mistakes from happening.
It is best to define exact steps to fix the issue. Defined steps make debugging easy. Schema errors work like broken contracts. If one thing is wrong, fix it before escalating it.
Note: Typed schemas are essential to prevent multi-agent workflow failures.
Lack of Specific Actions:
Multi-agents can fail even when the data is structured. It happens due to unclear instructions. The solution is to analyze the issue and add clear instructions.
An AI agent can get confused about:
- What closes the issue
- When to Assign
- When to Escalate
- When to do nothing
These issues seem reasonable for humans but not predictable.
An action schema is required to fix this issue. The action schema must define each allowed action.
Multi-agents require to return valid action to solve the issue. Invalid action results in escalation or retirement.
Note: Multi-agent failure happens when actions are unclear. It is best to define actions.
Optional MCP Rules:
Schemas only add value if they are enforced. Making rules optional reduces their value to suggestions. Make sure to add mandatory rules so that multiple agents must follow them.
Model Context Protocol (MCP) is the system that enforces mandatory schema rules.
Model Context Protocol (MCP) tells:
- What input allows
- What input is not allowed
MCP is required to prevent agents from inventing new fields. It is responsible for ensuring that multi-agents do not skip required inputs and cannot change formats.
Principles of Multi-Agent Systems:
You need to plan for failures. Check data at every step to ensure that you have not missed anything.
Before adding fields, make sure that it limits the possible actions. Keep a record of important steps.
Even though you did everything, still be ready for partial failures and retreats.
Note: In a multi-agent workflow, agents are a distributed system, not the chatbots.
Conclusion:
As an AI developer, your job is to ensure that the multi-agent workflow does not fail. You are responsible for creating a clear, structured multi-agent system.
Remember: AI agents can only work as reliable software components when they see type schemas, clear action definitions, and strict MCP enforcement.
Your takeaway is that you must take agents like code, not a chat conversation.
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