**When a Single AI Model Falls Short**
When we started building Audty's AI assistant, we did what most companies do: we connected a single large language model to our application and asked it to handle everything. It worked… sort of. Responses were generic, too slow, and costs were high.
Today, our platform uses a specialized agent architecture where each agent works as part of a team, using the optimal model and information level for its specific task. The result: more accurate responses, 60% faster and 45% more cost-effective.
This article explains how we achieved this, protecting key technical details while sharing the principles that led to our success.
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**The Problem: One LLM Can't Do It All**
Consider what goes into a workplace investigation:
When a single model tries to handle everything, each query carries massive context (laws, policies, evidence), leading to:
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**The Solution: An Architecture Inspired by Human Teams**
In a law firm, you don't ask the same lawyer to do everything: there are specialists for summaries, risk assessment, drafting reports… We applied the same logic to AI.
**General Architecture**
The system is organized around an intelligent router that analyzes user intent and directs the query to the most appropriate specialized agent. Each agent has access to a different context level (basic, standard, or full) and uses the AI model that best balances speed, accuracy, and cost for its function.
**Specialized Agents**
While the exact number and names of agents are part of our intellectual property, we can describe their generic roles:
Each agent receives only the information it needs: basic case data, or this plus relevant legislation, or the full context (including internal policies and case law). This avoids query overload and reduces costs.
**Why We Use More Than One AI Model**
No single model is perfect for everything. Some are extremely fast and cheap; others are slower but have advanced reasoning capabilities. Our architecture combines the best of several leading providers:
This combination lets us pay only for the power we truly need at each step.
**Intelligent Orchestration: The Conductor**
The system's core is a manager that decides in real-time which model should respond for each agent, based on the organization's configuration. If a provider fails (due to rate limits or errors), the manager automatically switches to a backup model without the user noticing. It also caches each client's preferences for greater efficiency.
**Tailored Context Levels**
Not all agents need access to the full legal database. That's why we created three context tiers:
This approach drastically reduces cost and latency because each query sends just the right amount of information.
**Smart Retrieval with Fallback**
To give agents access to current regulations, we use a retrieval-augmented generation (RAG) system that locates the most relevant articles for each query. If the primary engine fails, a secondary system based on traditional methods takes over, ensuring we never lack legal context.
**Legal Safeguards First**
Every response generated by our agents is subject to inviolable rules (guardrails) that ensure due process:
These rules prevent the AI from asserting guilt, ignoring the respondent's perspective, or inappropriately using terms like "victim" or "aggressor" categorically.
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**Measurable Results**
Migrating from a single model to a multi-agent architecture has delivered:
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**Lessons Learned**
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**Conclusion**
Using a single AI model for everything is like asking a generalist lawyer to handle a complex criminal case: they can do it, but it won't be optimal. Multi-agent orchestration with multiple AI providers enables:
At Audty, this architecture allows us to offer professional-grade AI assistance for workplace investigations in Chile, complying with due process and at an accessible cost for companies of all sizes.
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*Want to see this architecture in action? Request a demo at *[*audty.cl*](https://audty.cl/)* and discover how specialized AI can transform your workplace investigation management.*
