Era of AI Agents: A Different Dimension of Intelligence. Cloud for AI – Part 4

How AI Agents Work: The Cognitive Architecture of Digital Workers

Understanding AI agent architecture requires looking at it both technologically and through the lens of human functioning. Just as humans use reasoning to analyze and plan, senses to perceive, memory to store experiences, and the body to act, AI agents have their own digital equivalents.

Reasoning

At the heart of every Cloud for AI agent is the “brain”—a large language model acting as the reasoning engine. This isn’t just a text generator. With advanced prompt engineering and fine-tuning, it evolves into a structured thinking system, capable of contextual analysis and decision-making. It uses the chain-of-thought technique, so each decision is preceded by an explicit reasoning process that can be tracked and verified.

Perception

An AI agent’s perception system goes far beyond processing text queries. Cloud for AI agents implement multimodal perception—they can “see” through image analysis, “hear” via audio processing, and even “sense” system states by monitoring metrics and logs. These inputs are integrated into a unified situational representation, much like the human brain integrates sensory data into a coherent understanding of reality.

Memory

AI agent memory is a hierarchical information storage system. It consists of:

• a working memory, which is a fast in-RAM cache storing current context and recent interactions,
• a short-term memory, which is Redis-based storage holding data from recent hours or days,
• a long-term memory, which is vector databases such as Milvus or Qdrant for semantic storage and retrieval,
• a procedural memory, which is embedded in fine-tuned models and learned patterns, containing problem-solving “skills.”

Body

The execution system is the agent’s “hands”—tools and interfaces for real-world digital action. Agents can run SQL queries, call APIs, modify files, send messages, or even create and manage other agents. Tools come with schemas the agent can understand and apply in the right context, including both native and Model Context Protocol (MCP) tools.

Agent Frameworks in the Cloud for AI Ecosystem

Cloud for AI has been designed as a universal solution—one that doesn’t enforce a single approach to building agents but supports the best existing frameworks while adding its own layers of security, monitoring, and orchestration. In this context, frameworks refer to sets of tools, libraries, and ready-to-use software components that make it easier to create, run, and manage AI agents. Think of them as the “skeleton” or “platform” for building systems in which artificial intelligence performs specific tasks autonomously or semi-autonomously.

See how popular agent frameworks integrate with Cloud for AI and what capabilities this synergy offers.

LangChain

LangChain has become the standard for building applications based on Large Language Models (LLMs). It offers a rich set of abstractions for processing chains, agents, and tools. In Cloud for AI, LangChain is not only supported but also deeply integrated and enhanced with enterprise-grade capabilities.

Every LangChain agent launched in Cloud for AI automatically receives additional attributes. Most importantly, all agent operations are transparently logged and monitored. You do not need to modify your code—Cloud for AI automatically instruments LangChain agents with telemetry, metrics, and distributed tracing.

Processing chains in LangChain can also be optimized automatically. The system analyzes usage patterns and suggests—or directly applies—optimizations such as caching frequent prompts, parallelizing independent steps, or switching to smaller, faster models for simpler tasks.

CrewAI

CrewAI takes a unique approach to multi-agent systems, organizing agents into “crews” with defined roles and hierarchy. Cloud for AI preserves this model and extends it with added capabilities.

In Cloud for AI, each crew runs in its own container with precisely allocated resources—allowing hundreds of crews to operate in parallel with guaranteed performance and isolation. The system scales dynamically, reallocating resources in real time based on workload.

Role-based resource allocation in Cloud for AI allows you to precisely match resources to an agent’s role. Allocations are dynamic—the system monitors resource usage and can reallocate them in real time.

Cloud for AI analyzes historical agent performance data and can suggest optimal task assignments. For example, if the system notices that a particular researcher agent is especially effective in analyzing financial data, it will assign such tasks to them.

AutoGen

AutoGen from Microsoft makes it possible to create AI agents capable of engaging in natural, multi-party conversations. Cloud for AI integrates AutoGen with additional features that make these conversations more secure and productive.

Every AutoGen conversation in Cloud for AI is recorded and transcribed—complete with speaker attribution and timestamps—while preserving context. Transcripts are semantically indexed, making them fully searchable by keyword or meaning.

We are also currently working on support for Google Agent Development Kit and Agent2Agent.

Your Own Path: Building Custom Frameworks

Cloud for AI doesn’t lock you into ready-made frameworks. It provides a rich set of primitives and APIs to create highly specialized agent frameworks tailored to your organization.

The Agent SDK offers low-level modules—from memory management and tool execution to inter-agent communication and monitoring. You can combine them in any way, creating solutions perfectly tailored to your organization’s needs.

Thanks to the framework adapter layer, you can easily wrap your custom framework in Cloud for AI’s standard interfaces. This means your solution immediately gains access to all the platform’s features—security, scalability, and monitoring—without the need to rewrite your code.

Monitoring and Debugging Agents

Debugging regular software is hard enough; debugging autonomous AI agents is a different challenge altogether. One-off configuration and testing won’t cut it—real issues emerge only in production, where agents face unpredictable scenarios, evolving data, and real-world interactions.

The old post-mortem debugging model falls short. Instead, you need tools that provide real-time insight into an agent’s decision-making:

• Transparent reasoning. Agents should log their thought process before acting, including how they interpreted a query, what constraints they identified, what options they considered, and why they made a particular choice.
• Sandboxing critical actions. High-risk operations should first run in isolated environments to predict outcomes before execution.
• Checkpoints and rollbacks. The ability to pause agents at critical points for human review and to undo actions if needed.

The success of AI agent deployment depends less on their raw capabilities and more on the quality of monitoring and control. Research, like that from Gray Swan, shows AI agents are vulnerable—making continuous oversight essential. Organizations that invest in robust monitoring mechanisms will be able to use AI agents safely, while others risk operational and business disruption.

AI agents are not static programs—they’re dynamic systems that require ongoing observation, adaptation, and improvement. Only this approach allows organizations to scale their use safely in critical business areas.