What is an AI proxy?
one AI Agent is an autonomous software system that perceives its environment, interprets data, causes and performs actions to achieve specific goals without explicit human intervention. Unlike traditional automation, AI agents integrate decision-making, learning, memory and multi-step planning capabilities to fit complex real-world tasks. Essentially, AI agents act as a cognitive layer on data and tools that can intelligently navigate, transform or respond to situations in real time.
Why AI Agents Is Important in 2025
AI agents are now at the forefront of next-generation software architecture. As organizations want to integrate generative AI into their workflows, AI agents enable modular, scalable and autonomous decision-making systems. With multi-agent systems, real-time memory, tool execution and planning capabilities, agents are revolutionizing the industry from Devops to education. From static prompts to dynamic, the transition to target-driven proxy is as important as the leap from static websites to interactive web applications.
Types of AI Agents
1. Simple reflector
These agents operate according to the current perception, ignoring the rest of the perception history. They use conditional action rules (if statements) to functions. For example, the thermostat responds to temperature changes without storing previous data.
2. Model-based reflectors
These agents enhance reflex behavior by maintaining an internal state of dependence on perception history. The state captures information about the world and helps agents cope with some observable environments.
3. Target-based proxy
A goal-based agent evaluates future actions to achieve the desired state or goal. By simulating different possibilities, they can choose the most efficient path to meet a particular goal. Planning and search algorithms are crucial here.
4. Utilities-based agent
These agents not only pursue goals, but also consider the desirability of the results by maximizing utility functions. They are crucial in situations where trade-offs or probabilistic reasoning are needed (e.g., economic decision-making).
5. Learning Agent
Learning media continuously improves their performance by learning from experience. They consist of four main components: a learning element, performance element, critic (providing feedback), and question generator (providing exploratory actions).
6. Multi-mechanical system (MAS)
These systems involve multiple AI agents interacting in a shared environment. Each agent may have different goals and they may cooperate or compete. MAS can be used in robotics, distributed problem solving and simulation.
7. Agent LLM
These emerged in 2024-2025, and these are advanced agents powered by large language models. They combine features such as reasoning, planning, memory and tool usage. Examples include AutoGPT, Langchain Agents, and Crewai.
Key components of AI agents
1. Perception (input interface)
The perception module allows the agent to observe and interpret its environment. It processes raw input such as text, audio, sensor data, or visual feeds and converts it into internal representations.
2. Memory (short and long-term)
Memory allows the proxy to store and retrieve past interactions, actions, and observations. Short-term memory supports context retention in sessions, while long-term memory can continue across sessions to build user or task profiles. Usually implemented using vector databases.
3. Plans and decisions
This component enables the agent to define a series of actions to achieve the goal. It uses planning algorithms (e.g., thought trees, graph searches, enhanced learning) and can evaluate multiple strategies based on goals or utilities.
4. Tool usage and operation execution
Agents interact with APIs, scripts, databases, or other software tools to act in the world. The execution layer can handle these interactions safely and efficiently, including function calls, shell commands, or web navigation.
5. Reasoning and control logic
The reasoning framework manages how the agent interprets observations and decides actions. This includes logic chains, timely engineering techniques (e.g., React, COT), and routing logic between modules.
6. Feedback and learning cycle
The agent evaluates the success of its actions and updates its internal status or behavior. This may involve user feedback, task outcome assessment, or self-reflection strategies to improve over time.
7. user interface
For human proxy interactions, user interfaces (such as chatbots, voice assistants, or dashboards) can be performed through communication and feedback. It bridges natural language understanding and action interfaces.
Leading the AI Agent Framework in 2025
• Langchain
Use chains, tips, tool integration and memory to build a major open source framework for LLM-based proxy. It supports integration with OpenAI, Human, FAIS, weaving, web scraping tools, Python/JS execution, and more.
• Microsoft Autogen
A framework for multi-agent orchestration and code automation. It defines a unique proxy role (player, developer, reviewer) that communicates through natural language to implement collaborative workflows.
• Semantic kernel
Microsoft’s enterprise-level toolkit embeds AI using “skills” and planners. It is model-agnostic, supports enterprise languages (Python, C#), and integrates seamlessly with LLMs like Openai and Hugging Face.
• Openai Agent SDK (group)
Lightweight SDK defines proxy, tools, handovers and guardrails. It is optimized for GPT-4 and feature calls, and it enables structured workflows with built-in monitoring and traceability.
• Superagi
A comprehensive proxy operating system that provides the market for persistent multi-proxy execution, memory processing, visual runtime interfaces, and plug-in components.
• CREWAI
Crewai focuses on team-style orchestration, allowing developers to define dedicated proxy roles (e.g., planners, encoders, critics) and coordinate them in the pipeline. It integrates seamlessly with Langchain and emphasizes collaboration.
• IBM Watsonx Orchestration
A codeless enterprise SaaS solution for carefully planning a “digital worker” agent in a simple business workflow to drag.
Actual use cases of AI agents🌐
🔹Enterprise IT and Service Desk Automation
AI Agents simplify internal support workflows – reveal help desk tickets, diagnose issues and automatically resolve common issues. For example, agents like IBM’s Askit reduce IT support calls by 70%, while Atomicwork’s diagnostic agents support self-service troubleshooting directly in teams’ chat tools.
🔹Customer-oriented support and sales help
These agents handle numerous queries from order tracking to product suggestions by integrating with CRM and knowledge base. They promote user experience and deflect regular tickets. Example: Manage returns, process refunds and reduce support costs by about 65%. Explosive sales agents even increased the lead volume by about 50%.
🔹Contract and Document Analysis (Legal and Finance)
AI agents can analyze, extract and total data in combined and combined financial documents, reducing the time it takes to 75%. This supports departments such as banking, insurance and law, while fast, reliable insights are crucial.
🔹E-commerce and inventory optimization
Agents predict demand with minimal human supervision, track inventory and process returns or refunds. Walmart-style AI assistants and image-based product searches such as Pinterest Lens enhance personalized shopping experiences and conversion rates.
🔹Logistics and Operational Efficiency
In logistics, AI agents optimize delivery routes and manage supply chains. For example, UPS reportedly saves $300 million per year using AI-powered route optimization. In manufacturing, agents monitor device health through sensor data to predict and preempt crashes.
🔹HR, Finance and Backfield Workflow Automation
AI Agent Automation of Internal Tasks – From Processing Vacation Requests to Pay Query. IBM’s digital HR agent automates 94% of regular queries, greatly reducing HR workload. Agents also simplify invoice processing, financial settlement and compliance checks using document intelligence technology.
🔹Research, Knowledge Management and Analysis
AI agents support research by summarizing reports, retrieving relevant insights and generating dashboards. Google Cloud’s Generative AI Agent can convert large datasets and documents into conversational insights from analysts.
AI Agents and Chatbots and LLM
| feature | Chatbot | LLM | AI Agent |
|---|---|---|---|
| Purpose | Task-specific dialogue | Text generation | Target-oriented autonomy |
| Tool usage | No | Limited | Wide (API, code, search) |
| memory | Stateless | short term | Status + lasting |
| Adaptability | Predefined | Moderate adaptability | Feedback loop fully adaptive |
| autonomy | Responsive | Auxiliary | Autonomy + Interaction |
The future of proxy AI systems
The trajectory is clear: AI agents will become a modular infrastructure layer across enterprise, consumer and scientific fields. along with:
- Planning Algorithm (e.g., thought map, PRM-based plan)
- Multi-agent coordination
- Self-correction and assessment agent
- Continuous memory storage and query
- Tool security sandbox and character guardrail
…We hope that AI agents can mature into a co-pilot system that integrates decision-making, autonomy and accountability.
Frequently Asked Questions about AI Agents
Q: Is the AI agent just a prompt LLM?
one: no.
Q: Where can I build my first AI proxy?
one: Try Langchain templates, Autogen Studio, or Superagent, all of which are designed to simplify proxy creation.
Q: Will AI agents work offline?
one: Most rely on cloud-based LLM APIs, but local models (e.g. Mistral, Llama, Phi) can run agents offline.
Q: How to evaluate AI agents?
one: Emerging benchmarks include AARBENCH (task execution), AgentEval (tool use), and Helm (total evaluation).
in conclusion
AI agents represent the main development in AI system design, from passive generative models to active, adaptive and intelligent agents that can interact with the world. Whether you are automating DevOps, personalized education, or building smart assistants, the proxy paradigm can provide scalable and interpretable intelligence.
Michal Sutter is a data science professional with a master’s degree in data science from the University of Padua. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels in transforming complex datasets into actionable insights.
