The construction of an intelligent agent goes far beyond the rapid engineering of language models. To create autonomous real world systems that can think,, reason,, actAnd learnYou must design a complete solution which orchestrates several closely integrated components. The following seven layer frame is a mental model tested in combat for any serious person about the development of AI agents – whether you are founder, IA engineer or product manager.
1. Layer experience – Human interface
The experience layer acts as the point of contact between humans and the agent. It defines how users interact with the system: conversation (cat / web / app), voice, image or even multimodal commitment. This layer must be intuitive, accessible and capable of precisely capturing the intention of users, while providing clear comments.
- Basic design challenge: Translate ambiguous human goals into machine objectives and understand.
- Example: A customer support chatbot interface or a voice assistant in a smart house.
2. Discovery layer – Gather information and context
The agents must orient themselves: know what to ask, where to seek and how to collect relevant information. The discovery layer includes techniques such as web research, document recovery, data exploration, context collection, integration of sensors and analysis of interaction history.
- Basic design challenge: The recovery of efficient, reliable and contextual information which reveals only what matters.
- Example: Search for product manuals, extract knowledge bases or summarize recent emails.
3. COMPOSITION COMPOSITION OF AGENTS – structure, objectives and behaviors
This layer defines What The agent is and how It should behave. It includes the definition of the agent's objectives, its modular architecture (sub-agents, political, roles), possible actions, ethical limits and configurable behaviors.
- Basic design challenge: Activation of personalization and extensibility while ensuring consistency and alignment with the objectives of the user and the company.
- Example: Set up an assistant sales agent with negotiation tactics, a brand voice and climbing protocols.
4. Raising and planning layer – The agent's brain
At the heart of autonomy, the reasoning and planning layer manages logic, decision -making, inference and action sequencing. Here, the agent assesses the information, weighs the alternatives, the stages of plans and adapts strategies. This layer can take advantage of symbolic reasoning engines, LLM, classic AI planners or hybrids.
- Basic design challenge: Go beyond the correspondence of models to a real adaptive intelligence.
- Example: Prioritize customer requests, plan workflows in several stages or generate argument channels.
5. Tool & Api Layer – Acting in the world
This layer allows the agent to carry out real actions: execute code, trigger APIs, control the IoT devices, manage files or run external workflows. The agent must interface safely with digital and (sometimes) physical systems, often requiring error management, authentication and robust authorization management.
- Basic design challenge: The safe, reliable and flexible action with external systems.
- Example: Reserve a meeting on your calendar, place an electronic commerce order or execute data analysis scripts.
6. Memory and feedback – Contextual recall and learning
Agents who learn and improve over time must maintain memory: follow previous interactions, store the context and incorporate user comments. This layer supports both a short -term contextual reminder (for conversation) and long -term learning (improve models, policies or knowledge bases).
- Basic design challenge: Representation of evolutionary memory and effective feedback integration.
- Example: Remember user preferences, learning common support problems or refining suggestions in an iterative way.
7. Infrastructure layer – scale, orchestration and safety
Under the application battery, a robust infrastructure guarantees that the agent is available, responsive, scalable and secure. This layer includes orchestration platforms, distributed calculations, surveillance, tips and compliance guarantees.
- Basic design challenge: Reliability and large -scale robustness.
- Example: Manage thousands of instances of simultaneous agent with availability guarantees and secure API bridges.
Main to remember
- True autonomy requires more than understanding language.
- Integrate the 7 layers For agents who may feel, plan, act, learn and evolve safely.
- Adopt this framework To assess, design and build new generation AI systems that solve significant problems.
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Michal Sutter is a data science professional with a master's degree in data sciences from the University of Padova. With a solid base in statistical analysis, automatic learning and data engineering, Michal excels in transforming complex data sets into usable information.
