Autonomous AI agents: how to architect them without losing control
AI agents are both exciting and concerning. How do you design agentic systems that are reliable, observable, and secure in production? Concrete lessons from the field.
Autonomous AI agents are the topic of the moment. After years of rigid chatbots, the idea that a system can plan, execute actions, and self-correct is genuinely compelling. But behind the excitement lies demanding technical reality.
I have deployed agentic systems in production at Thales (code generation agents, search agents, natural language database Q&A). Here is what I learned.
What is an agent, really?
An agent is not an LLM with tools. It is a decision-making system that:
- Receives an objective (not a single instruction)
- Plans a sequence of actions
- Executes those actions via tools
- Observes results
- Iterates until the objective is achieved (or fails gracefully)
The key point: the LLM is the reasoning engine, not the complete system.
Architecture patterns that work
ReAct (Reasoning + Acting)
The most robust pattern for single-task agents. The LLM alternates between:
- Thought: reasoning about the current state
- Action: calling a tool
- Observation: result of the action
Simple, debuggable, reliable. This is my starting point for 80% of use cases.
Plan-and-Execute
For complex tasks requiring planning:
- A "planner" LLM decomposes the task into sub-tasks
- Specialized agents execute each sub-task
- A "synthesizer" LLM aggregates the results
More powerful, but harder to debug.
Multi-agent with orchestrator
For broad domains requiring different areas of expertise. An orchestrator delegates to specialized agents (Code Agent, Search Agent, Data Agent...).
This is the architecture we deployed at Thales for AI Core Services.
Classic mistakes
Giving the agent too much freedom
An agent with 30 tools and no constraints will explore unpredictable paths. Limit the number of tools, define clear boundaries on what it can and cannot do.
Forgetting the circuit-breaker
An agent can loop indefinitely. Always implement:
- A maximum number of iterations
- A global timeout
- A maximum token budget
Ignoring observability
Without detailed traces of each reasoning step, debugging becomes a nightmare. Log everything: each thought, each tool call, each observation.
Security of agents in production
This is the most critical and most underestimated point.
Prompt injection: a malicious user can embed instructions in data processed by the agent. Always validate inputs, isolate untrusted data from the system context.
Privilege escalation: an agent should never have access to more than it needs for its task. Principle of least privilege, even for AI tools.
Human-in-the-loop for irreversible actions: database writes, email sending, external API calls — these actions must be confirmed by a human or protected by strict guardrails.
What I use in production
Orchestration: LangChain (Agents) or custom code depending on complexity
Observability: LangSmith + structured logs
Deployment: FastAPI / containerized on K3s
Rate limiting: Redis to prevent costly loops
Evaluation: golden datasets + automated regression tests
The question to ask before deploying
"If this agent does whatever it wants for 10 minutes unsupervised, what is the worst thing that could happen?"
If the answer worries you, strengthen your guardrails before going to production.
AI agents are a transformative technology — but "autonomous" should not mean "uncontrollable." Architecture, security, and observability are not afterthoughts: they are the conditions for trust.
Do you have an agentic project? Let's talk.