LLMs like ChatGPT, Claude, Gemini etc. have evolved beyond simple pattern matching. They can engage in complex problem-solving, generate sophisticated code, and perform nuanced analysis. However, they still operate primarily in a reactive manner, responding to specific prompts and instructions. AI agents represent the next evolution: autonomous systems that can proactively pursue complex goals, engage in decision-making and dynamically adapt their approach based on the situation.
AI's leading organizations offer complementary definitions of agents. OpenAI defines them as "systems that adaptively pursue complex multi-step goals through reasoning, with autonomous decision-making not predetermined at every step." Bill Gates emphasizes their interface capabilities as "software that responds to natural language and can perform a variety of tasks based on user knowledge," while NVIDIA focuses on their architecture as "systems with complex capabilities for reasoning, memory, and task execution."
What distinguishes agents is their ability to interact with external environments through tools and adjust behavior based on outcomes. Consider email management: while traditional AI assists with drafting responses on request, an agent can autonomously monitor inboxes, prioritize messages, generate contextually appropriate responses, and coordinate follow-ups – all while maintaining consistency across multiple systems and conversations.
This autonomous operation capability transforms how businesses implement AI. Rather than requiring explicit instructions for each step, agents can interpret broader objectives and pave their own execution path. They seamlessly integrate across technology stacks, from CRM platforms to document management systems, minimizing the need for custom development per integration.
Core Components of AI Agents
AI agents can be designed with various architectures, depending on the use case. However, most implementations share several common components that enable reasoning and task execution capabilities. Let's examine them while keeping in mind that their exact implementation and integration patterns may vary significantly based on specific requirements.
LLM Сore
The heart of every AI agent – a foundation model – typically an LLM like GPT, Claude, or Gemini, providing reasoning and decision-making capabilities. Some specialized implementations might opt for smaller, domain-specific models when the task requirements are more focused.
Orchestrator
It coordinates all other components, managing information flow, processing user inputs, and scheduling tasks while maintaining the state. In multi-agent systems, the orchestrator also handles communication between different agents, enabling collaborative problem-solving.
Knowledge Base
Agentic systems need the access to relevant information to make informed decisions. They typically combine multiple knowledge sources: foundation model's pre-trained knowledge, external knowledge bases connected through retrieval systems (RAG), web search information, user input and history of interactions.
Tools Integration
AI agents interact with the world through tools and integrations. These include API connections, data processing capabilities, and specialized functions for industry-specific tasks. The specific toolset is tailored to the agent's purpose and operating environment.
For instance, a Customer Support agent typically comes equipped with:
- ticket management system API for creating and updating support tickets;
- knowledge base search tool to find relevant documentation;
- customer DB interface to check account history and status;
- email API for sending notifications;
- sentiment analysis tool to understand customer satisfaction.
The following diagram illustrates the interaction of the core components of an AI agent, showing how the model processes requests, handles data, and operates in different environments.
How does this system work?
- The user interacts with the LLM Core, sending a request.
- The model processes the request by accessing the Knowledge Base to retrieve relevant information.
- If needed, the system utilizes Tools to perform a specific task or obtain additional data.
- The result is passed to the Environment, where the agent's decision is applied in practice.
- After the interaction is complete, the agent can receive Feedback, helping it improve efficiency for future requests.
This diagram clearly demonstrates how an AI agent integrates natural language processing, knowledge access, automation tools, and external environment interaction to operate effectively.
Levels of Agency in AI Systems
Based on the HuggingFace article, AI systems can have different levels of independence, with each level representing a significant leap in autonomy and capability. The choice of agency level determines system architecture, development costs, computational requirements, and potential applications – from simple content generation to autonomous multi-agent systems.
Level 1: Simple AI Processor
The most basic form of LLM integration, where the model's output is processed without affecting program flow. At this level, the system operates similarly to traditional rule-based software, with the LLM serving as a sophisticated text processor.
Capabilities and Use Cases
- Suitable for content generation, translation, and basic Q&A.
- Limited to predefined sequences with no deviation.
Examples: Simple chatbots, content filters, basic text processors.
Level 2: Router
Introduces basic decision-making capabilities, where the LLM's output determines program flow through predefined paths. This level enables simple adaptive behaviors while maintaining predictable execution patterns.
Capabilities and Use Cases
- Handles deterministic tasks with clear decision points.
- Routes inputs to appropriate processing pipelines while maintaining predictable execution paths.
Examples: Customer intent classifier (sales vs. support vs. billing), language detector for multilingual systems, email priority router.
Level 3: Tool Caller
Represents a significant leap in capability, enabling the LLM to select and use tools dynamically. The system can choose appropriate functions and parameters based on context, enabling more complex problem-solving.
Capabilities and Use Cases
- Dynamically selects and chains multiple tools based on context.
- Adapts tool selection based on intermediate results and error states.
- Can handle complex sequences of tool interactions while maintaining context.
Examples:
- Data analyst combining SQL queries, visualization tools, and statistical functions.
- Code assistant using git operations, test runners, and documentation generators.
- Research agent leveraging search APIs, PDF parsers, and citation tools.
Level 4: Multi-step Agent
Introduces true autonomy in execution flow, allowing the LLM to control program continuation and make sequential decisions. These agents maintain context and adapt their approach based on previous steps.
Capabilities and Use Cases
- Autonomously plans and executes multi-stage tasks with state management.
- Maintains long-term context and learns from previous execution steps.
- Can backtrack and adjust strategy when encountering obstacles.
- Self-monitors progress and effectiveness of chosen approaches.
Examples:
- Scientific researcher formulating hypotheses, designing experiments, analyzing results, and adjusting research directions.
- Customer support agent handling complex cases through multiple interactions, ticket updates, and internal escalations.
- Software architect breaking down requirements into tasks, writing code, running tests, and iteratively refining based on results.
Trading agent monitoring market conditions, analyzing trends, adjusting strategies, and executing sequences of trades.
Level 5: Multi-Agent System
The most advanced form of agency, where agents can create and coordinate with other agents. This enables complex collaborative behaviors and sophisticated problem-solving strategies.
Capabilities and Use Cases
- Superhuman performance in specialized domains through agent collaboration.
- Can decompose complex problems and delegate subtasks to specialized agents.
- Implements sophisticated coordination protocols between agents.
- Maintains global context while allowing parallel execution.
- Handles resource allocation and priority management across agent network.
Examples:
- Research team with specialized agents for literature review, experiment design, data analysis, and paper writing, coordinating to produce comprehensive studies.
- Software development system with architect agent spawning specialized agents for frontend, backend, testing, and DevOps, managing complex project delivery.
- Business intelligence system combining market research agents, data analysts, and strategy advisors to generate actionable insights.
When to Use AI Agents
When building applications with LLMs, the key is finding the simplest solution possible and only increasing complexity when needed. While agents offer powerful capabilities, they often trade latency and cost for better task performance – a tradeoff that needs careful consideration.
The fundamental question is: do you really need flexibility in your workflow? Consider a document management system where employees handle various contracts and agreements. If most requests involve standard documents – like processing NDAs or generating template-based agreements – implementing agents would be overkill. A simple system where users either select from document templates or submit basic information through forms might be perfectly sufficient. In this case building a traditional system gives you 100% reliability without the complexity and unpredictability that comes with LLMs.
However, real-world scenarios often defy such simple categorization. Consider this request: "We need to update our partner agreement to include new data handling requirements from both EU and APAC regions, ensure it aligns with our updated cybersecurity policies, and add specific performance metrics while keeping the risk allocation balanced." This query spans multiple domains: legal compliance, security requirements, performance standards, and risk management. No predetermined decision tree can adequately handle such interconnected concerns.
When you find your predefined workflows falling short too often, that's when agents become valuable. For the complex contract request above, you could implement an agent with access to multiple tools: legal clause database, compliance checkers, policy verification tools, and a RAG system for company standards and regional regulations. The agent can dynamically coordinate these tools while maintaining context throughout the document creation and review process.
Current State of AI Agents in Business
More and more AI influencers keep claiming that 2025 will be a year of AI agents. The business adoption of AI agents is accelerating rapidly, with major developments happening across different sectors. Organizations are finding innovative ways to leverage agent technology for improved efficiency and customer experience.
Computer Use Agents
One of the most significant recent developments are computer-using agents – AI systems that can interact with computer interfaces just like humans do. CUA represents a major step forward in agent autonomy, allowing AI to operate existing software and websites without requiring special integrations or APIs.
The technology first appeared with Claude's Computer Use capability in October 2024, followed by OpenAI's Operator in January 2025. These systems can perform a wide range of tasks like navigating websites, filling out forms, processing documents, and managing digital workflows – all while using standard computer interfaces. Both technologies are in their early phases, meaning that their potential applications could dramatically expand in the future
Johnson & Johnson: Drug Discovery Automation
J&J employs AI agents to optimize chemical synthesis in drug discovery. These agents determine optimal conditions for solvent switching processes, significantly accelerating drug development while working alongside traditional machine learning systems and digital twins. The company maintains human oversight to prevent potential biases and hallucinations in agent outputs.
Moody's: Multi-Agent Financial Analysis
Moody's has developed a sophisticated system of 35 specialized agents that work together on financial analysis tasks. These agents, each with specific personalities and data access, can analyze complex scenarios like company financial health assessment. Their multi-agent approach allows for diverse perspectives and disagreement, mimicking human analytical processes.
eBay: Development and Sales Support
eBay has created its own agent framework that orchestrates multiple LLMs for various tasks. Their agents assist in code writing, marketing campaign creation, and will soon help buyers find items and sellers list goods. The system adapts to individual employee preferences over time, enhancing productivity across different departments.
Deutsche Telekom: Internal Knowledge Management
The telecommunications giant has deployed an AI agent called askT that handles employee queries about internal policies and benefits, serving 10,000 employees weekly. The system is expanding to perform tasks like vacation requests directly in HR systems, demonstrating how agents can streamline administrative processes.
Cosentino: Customer Service Automation
This surface materials manufacturer has implemented a "digital workforce" of AI agents in customer service. These agents undergo training similar to human employees and follow strict processes. They've successfully automated order processing tasks previously requiring 3-4 human staff members, allowing those employees to focus on more complex service areas.
The rapid evolution of agent technology and growing number of successful implementations suggest we're at the beginning of a significant transformation in how businesses operate. As these technologies mature and become more accessible, we expect to see even broader adoption across industries and applications.
Best Practices and Implementation Guidelines
Implementation of AI agents requires careful planning and a structured approach. Based on experiences from early adopters and industry leaders, several key practices have emerged as crucial for effective agent deployment.
Start Small
The most successful agent implementations start with well-defined, contained use cases where success can be clearly measured. Cosentino demonstrated this approach by focusing initially on specific customer service workflows. This approach allows organizations to build confidence in agent capabilities. It also helps identify and address any challenges early, creating valuable templates for future deployment.
Establish Safety Controls
Proper oversight mechanisms are essential, as demonstrated by Johnson & Johnson's approach to drug discovery agents. Organizations should implement multiple layers of control in their agent systems. This means establishing clear task limitations, requiring human validation for critical decisions, and maintaining systematic output verification. Every agent action should be tracked through comprehensive audit trails, and regular performance reviews should be conducted to ensure continued safety and efficiency.
Design Robust Architecture
Organizations need to focus on building flexible APIs for tool integration and implementing robust state management systems. The architecture should establish clear protocols for agent-to-agent communication and include comprehensive monitoring and logging capabilities. Equally important is the implementation of fallback mechanisms for handling potential failures or unexpected situations.
Prepare for Growth
While starting small is important, the underlying architecture should be designed with scale in mind. Moody's approach of developing specialized agents that can work together demonstrates the power of scalable systems. This means creating modular agent designs with clear interfaces between components. Organizations should focus on building reusable tool integrations and designing for multi-agent coordination from the start. The infrastructure should be capable of handling increased computational demands as the system grows.
Embrace Continuous Improvement
The development of agent systems is inherently iterative. Organizations need to establish clear success metrics and conduct regular performance evaluations. User feedback should be systematically collected and analyzed to guide improvements. Error analysis should be an ongoing process, feeding into continuous system refinement.
The key to success with Agents lies in finding the right balance between automation and control. Organizations should progress from simpler agency levels to more complex implementations only when simpler approaches prove insufficient. The ultimate goal is to ensure that agents enhance existing processes rather than complicate them, delivering tangible business value while maintaining operational stability.
Conclusion
As AI agents transform from experimental technology to essential business tools, organizations face both opportunities and challenges. At Anadea, we combine deep technical expertise in AI development with practical implementation experience to help businesses navigate this transformation. Whether you're looking to automate complex workflows, enhance decision-making processes, or build innovative customer experiences, our team can help you design and implement agent solutions that deliver real business value. Get in touch with us — we’d be happy to explore how we can help.
