Beyond chatbots: what autonomous AI agents really do
A chatbot answers questions. An AI agent, makes decisions, plans action sequences, and executes end-to-end tasks, without human intervention at each stage. This is the difference between a tool that assists and a system that works.
Specifically, an AI agent can receive a complex request ("prepare the monthly commercial report with CRM data and the latest industry trends"), break it down into sub-tasks, query your tools, analyze the results, and produce the final deliverable, without anyone having to coordinate each step. Models like Claude 3.5 Sonnet (Anthropic) or GPT-4o (OpenAI) have developed multi-step reasoning capabilities that make this possible on an enterprise scale.
Multi-agent systems go further: several specialized agents collaborate, question each other, and delegate tasks. A "research" agent feeds an "analysis" agent which feeds a "writing" agent, all orchestrated in a few seconds. We design and deploy these architectures in your environment, with your security constraints.
The 4 types of deployed agents
Business co-pilot (RAG)
An AI assistant trained on your internal document base: contracts, procedures, reports, emails. Answers your teams' questions with verifiable sources.
- Indexing of your internal documents
- Sourced and traceable responses
- Integration Notion, SharePoint, Drive
- Gemini 1.5 Pro or Mistral for research
Autonomous agent (complex tasks)
An agent that receives an objective and executes it from start to finish: data collection, analysis, writing, sending. Zero human intervention on standard cases.
- Claude 3.5 Sonnet for long-range reasoning
- GPT-4o for multimodal tasks
- Connection to your APIs and databases
- Audit logs for each action
Multi-agent system
Architecture with multiple specialized agents collaborating: a planning agent orchestrates executing agents. For complex workflows with high volume.
- Orchestration via LangGraph or AutoGen
- Domain-specific specialized agents
- Backpressure and integrated error handling
- Scalable according to the volume of tasks
Sovereign Private LLM
Deployment of an open-source model (Mistral Large, Meta Llama 3, Qwen) on your infrastructure. Data is entirely private, with zero dependence on a cloud provider.
- Mistral AI for European compliance
- Meta Llama 3 for complete on-premise
- Fine-tuning on your proprietary data
- Zero marginal cost per use
AI models by area of excellence
| Model | Editor | Area of Excellence |
|---|---|---|
| Claude 3.5 Sonnet | Anthropic | Complex reasoning, multi-step agents, nuanced analysis, long-form writing |
| GPT-4o | OpenAI | Multimodal (images, PDF, audio), understanding heterogeneous documents, OCR vision |
| Gemini 1.5 Pro | RAG documentary, context window 1M tokens, Google Workspace search | |
| Perplexity | Perplexity AI | Real-time monitoring, sourced web research, automated competitive benchmarking |
| Mistral Large | Mistral AI | European sovereignty, on-premise France, native GDPR compliance |
| Meta Llama 3.1 | Meta | 100% private deployment, fine-tuning, zero marginal cost, open-source |
| Qwen 2.5 | Alibaba | Multilingual contexts, Asian language processing, sovereign alternative |
| DeepSeek R1 | DeepSeek | Structured reasoning, financial and technical analysis, extremely low cost at volume |
Our approach is rigorously agnostic: we always select the model that corresponds to your use case, your GDPR constraints and your budget - not the one that generates the best commissions.
Sovereignty and GDPR compliance : our basic commitment
Each AI agent deployment at Quantum Consulting integrates from design:
- No customer data sent to third-party models without a signed DPA (Data Processing Agreement)
- On-premise deployment option for sensitive data (health, finance, legal)
- Complete audit trail of each action performed by an agent
- Configurable human validation circuit for high-impact actions
- AI governance documentation provided with each deliverable
How an AI agent deployment is conducted
Definition of the use case and constraints
What process ? What data ? What security constraints ? What actions can the agent take autonomously and which ones require validation ? This step frames everything that follows.
Selection of model and architecture
Benchmark of models available for your real use case. Selection based on performance, cost, latency, and sovereignty constraints. Design of the architecture (simple agent, RAG, multi-agents).
Development & integration
Construction of the agent with connection to your APIs, databases, and business tools. Development in an isolated environment with representative test data sets.
Red team & robustness tests
Prompt injection attempts, out-of-distribution behavior tests, guardrail verification. No agent is deployed to production without having been attacked by our own testers.
Deployment & monitoring
Production deployment with monitoring dashboard: success rate, latency, cost per task, detected anomalies. Automatic alerts in case of unexpected behavior. Follow-up at 30 and 90 days.
Frequently Asked Questions about AI agents and private LLMs
What is the difference between a hosted LLM (API) and a private LLM ?
A hosted LLM (ChatGPT, Claude.ai) processes your prompts on the provider's servers, your data transits there. A private LLM (Mistral on-premise, Llama 3) runs on your infrastructure or dedicated cloud: your data never leaves. For regulated sectors (health, finance, defense), the private LLM is often the only legal option.
Can we change the model without rebuilding everything?
Yes - provided the architecture was designed with this flexibility. We build our agents with an abstraction layer between business logic and the LLM model. Switching from Claude to GPT-4o (or to Mistral to switch to sovereign) only requires changing the connector, not rewriting the entire logic.