Key Reliability Challenges in Multiagent Systems

 

1. Non-determinism and unpredictable behaviour

One of the most fundamental challenges is non-determinism. This means the same input can produce different outputs on different runs.

• What it is: An agent may choose a different plan, select a different tool, or generate a different response even when faced with the exact same initial conditions. This unpredictability stems from the probabilistic nature of the underlying language models.
• Real-world example: A financial advisory agent is asked to create a retirement plan based on a client's profile. On Monday, it suggests a conservative, low-risk portfolio. On Tuesday, given the identical profile, it recommends a high-risk strategy focused on emerging markets. This inconsistency makes it impossible to trust the agent's recommendations for critical financial decisions.

2. Error Cascading and Compounding Failures

In a multi-step process, a small, almost undetectable error at the beginning can amplify as it moves through the workflow, leading to a massive failure.

• What it is: An early mistake is not caught and is instead passed on to the next agent in the chain, which then makes its own decisions based on flawed information. The error snowballs with each subsequent step.
• Real-world example: In an automated procurement system, an agent slightly misinterprets a product specification, ordering a component that is off by just a few millimeters. The next agent, responsible for logistics, arranges shipping for this incorrect part. The assembly agent then tries to use the part, causing the entire production line to halt. A tiny initial error results in costly downtime and delays.

3. Hallucinations in Planning and Reasoning

Agents can confidently invent facts, create invalid plans, or imagine non-existent API responses. This is known as hallucination, and it's particularly dangerous during long-horizon planning.

• What it is: The agent generates plausible but entirely false information and acts on it as if it were true. It's not just getting a fact wrong; it's fabricating reality.
• Real-world example: A healthcare administration agent is tasked with verifying a patient's insurance coverage for a specific procedure. Instead of checking the actual insurance provider's database, it hallucinates a "pre-authorized" confirmation code. The hospital proceeds with the procedure, only to find out later that it was never approved, leading to a significant financial loss and a billing nightmare for the patient.

4. Poor Task Decomposition

The ability to break a large, complex goal into smaller, logical sub-tasks is crucial for agentic systems. Unfortunately, they often struggle with this.

• What it is: Agents break down a goal into a series of steps that are incomplete, ordered incorrectly, or logically inconsistent.
• Real-world example: A project management agent is assigned the goal of "launching a new marketing campaign." It creates sub-tasks to "run social media ads" and "send email newsletters" but completely omits the critical preliminary step of "defining the target audience and messaging." The resulting campaign is disjointed and ineffective because it was built on a flawed plan.

5. Tool Misuse or Over-Use

Agents are often given access to tools like databases, APIs, or internal software. However, they frequently use these tools incorrectly.

• What it is: An agent might call an API with the wrong parameters, query a database inefficiently, or use a tool in a sequence that causes unintended side effects.
• Real-world example: A customer support agent is designed to use a knowledge base API to answer user questions. When it can't find an answer, it gets stuck in a loop, calling the same API hundreds of times per second. This triggers a system-wide slowdown, impacting all other users and potentially taking the entire support platform offline.

6. Lack of Robust Self-Verification

Most agentic systems lack a reliable mechanism to check their own work. They may complete a task and report success without ever verifying if the outcome was actually correct.

• What it is: Agents proceed with confidence, assuming their reasoning, data retrieval, and final outputs are accurate without any internal quality control.
• Real-world example: A data analysis agent is tasked with generating a quarterly sales report. It successfully pulls data and creates a beautiful chart showing a 20% increase in sales. However, it failed to notice that a data filter was applied incorrectly, excluding all sales from the West Coast region - producing an erroneous output that hinders the rest of the agents. The report is presented to leadership, leading them to make strategic decisions based on a completely inaccurate picture of the business.

 

Conclusion: A Path Toward Reliable Multiagent Systems

The challenges outlined above are well understood across the industry - and critically, they are engineerable problems, not the inextricable limitations of multiagent systems. Reliability does not emerge by chance in agentic architectures; it must be deliberately designed and continuously validated.

By layering patented reliability mechanisms on top of AI agents - spanning deterministic orchestration, causal validation, error isolation, self-verification, and controlled tool execution - we have transformed inherently probabilistic systems into dependable Digital Workers. These reliability layers allow agents to collaborate without compounding errors, verify their own outputs, and consistently execute complex workflows in high-stakes environments.

Reliability in multiagent systems is difficult, but with the right architectural foundations, Digital Workers can be trusted to deliver measurable outcomes at scale - unlocking their full potential as a new class of dependable, autonomous collaborators.