The great AI debate: Wrappers vs. Multi-Agent Systems in enterprise AI [Chapter 2 - AI Deep Dives]
George
Chief of AI at Moveo
September 5, 2025
in
✨ AI Deep Dives
Welcome back to the "AI Deep Dives" series! In Chapter 1, we showed that RAG grounds answers in your content. This is helpful, but RAG alone doesn’t run a business process. This chapter covers how the Agent's behavior is orchestrated in production: one mega-prompt (Wrapper) vs. a governed Multi-Agent system.
Now, we’re taking the discussion a step further. If RAG is about how an LLM accesses information, the next question is: How do we control what an AI agent does in production, step by step, under your policies and SLAs? Two philosophies dominate:
Wrapper (mega-prompt): cram docs, rules, and tool hints into one prompt; hope one model does everything in one shot.
Multi-Agent System: let specialized agents plan, retrieve, converse, validate, and log, while dialog flows enforce the order of operations and permissions.
Your choice determines whether you get occasional wins or consistent, compliant outcomes. Let's explore this further!
Many companies, when taking their first steps with LLMs, opt for what we call the Wrapper approach. The idea is straightforward: put all the information and instructions into one single, massive prompt, hoping the model will do all the work.
Imagine a “mega-prompt” that contains company documentation, business rules, and task specifications, all mixed together. This might seem quick and easy for demos, but it’s a path full of pitfalls.
Why is it risky?
This approach creates an opaque and fragile system. The prompt's complexity makes it difficult to maintain and audit. If a business rule changes or a new document is added, the entire prompt has to be rewritten, increasing the risk of errors and unexpected behavior.
It’s like running a restaurant where one person cooks, takes orders, sources ingredients, plates, and does quality checks from a 50-page binder. Professional restaurants, like enterprises, rely on specialists with clear roles and workflows, and AI systems should do the same: use specialized agents with defined responsibilities instead of overloading a single mega-prompt.
When moving from a simple demo to a real-world enterprise application, the stakes change dramatically. While a wrapper may seem easy to implement, its lack of structure creates critical business and operational liabilities. The following points detail what enterprises truly care about and where the wrapper approach consistently falls short:
Process order: Prompts don’t encode workflows, and models can (and do) skip steps.
Change control: Business logic buried in natural-language prompts, with no versioning, no peer review.
Auditability: You can’t prove consent, disclosure, or OTP occurred in the right order.
Predictability: Tiny wording changes → different outcomes; shaky SLAs.
Compliance: Tone/policy drift; jailbreak exposure under pressure.
Cost & latency: Long contexts and retries inflate tokens and response times.
In short, while wrappers provide a quick path to a proof of concept, their simplicity is a dangerous illusion in a production environment. They may appear enterprise-ready, but they fundamentally lack a governance model. For any critical business workflow where compliance, predictability, and control are non-negotiable, this fragility makes the approach unsustainable.
If you haven't read the first chapter of "AI Deep Dives Series" yet, click here.
The path of specialization: Multi-Agent Systems
In contrast to the "one-part-does-it-all" approach, a Multi-Agent Systems architecture emerges. Instead of overloading a single LLM, this approach treats it as a valuable component, but not the only one, within a team of specialists.
Each “agent” is a module with a clear function, working together to solve a complex task in a planned, observable, and governable way.
Why is it advantageous?
In a Multi-Agent System, each LLM or module is a specialist. For example, one agent might be responsible only for query decomposition, while another handles the search for relevant documents (RAG), and a third focuses on compliance validation.
This division of labor makes the system much more robust and predictable.
Instead of asking one model to do everything, Multi-Agent Systems give you specialized agents:
A Planning Agent decides what workflow to follow.
A Response Agent talks with the user and pulls from the right sources.
Other agents check for compliance and insights.
This way, you get the best of both worlds:
Deterministic systems (rules, workflows, required steps like consent → OTP → action) that guarantee order and compliance.
LLMs (flexible language and understanding) that make the interaction natural and human-like.
Together, they create reliable, auditable processes that scale—something a single mega-prompt can never deliver.
A practical example: from theory to reality
To better understand this difference, let's analyze the example of a customer asking: “Why was I charged $200 at Whole Foods yesterday?”
How it would work with the Wrapper approach
Following this approach, everything goes into one giant prompt—policies, account data, instructions, tools—and the model tries to answer in one shot. Sometimes it works, but sometimes it skips a step, fabricates details, or gives an inconsistent answer. There’s no guarantee of order or compliance.
For example, in the “Why was I charged $200 at Whole Foods yesterday?” case, the model might go straight to creating a dispute before verifying user consent or authentication. These errors show how Wrappers can break process order in ways that are unacceptable or even dangerous in enterprise settings.
How it would work with Multi-Agent Systems
In a Multi-Agent System, the workflow would be orchestrated:
The Query Decomposition Agent decomposes the user’s query for optimal processing
The Planning Agent decides the next step is to execute the “Dispute Transaction” workflow and fetches the most relevant Transaction policies.
The Response Agent uses an LLM to interact with the user, while also triggering an advanced RAG pipeline to utilize refund policies and transaction details.
The Workflow Agent enforces the right sequence: consent → OTP verification → create dispute.
The Compliance Agent verifies the final response to ensure it aligns with company policies.
Finally, the Insights Agent extracts structured information for the system, such as the “disputed charge” status.
In short, each agent acts as a specialized supervisor, ensuring the response is not only accurate but also secure and compliant with the rules.
Choose the path of trust and governance
The message is clear: while the Wrapper approach may be a shortcut for quick tests, it's not a sustainable foundation for building enterprise-scale AI solutions.
To achieve consistent, reliable, and auditable results, the Multi-Agent Systems architecture is the strategic choice. It allows companies to plan, observe, and govern their AI solutions, transforming them from fragile tools into essential business assets.
Stay tuned for the next chapter of our "AI Deep Dives" series, "Chapter 3: The Problem with Prompt & Pray", where we’ll explore more about how to build and optimize these complex systems.