How to build an AI agent: a dive into autonomous intelligence
Artificial Intelligence agents are autonomous intelligent systems designed to perform specific tasks using advanced machine learning mechanics, including large language models (LLMs). These AI agents have already become integral to the modern IT landscape, driving automation across various industries.
AI agents operate based on predefined objectives and adaptive learning mechanisms, enabling them to function without explicit human guidance. Their capacity for contextual decision-making has allowed them to evolve from simple scripted bots to sophisticated intelligence systems.
According to Roots Analysis, the global AI agent market is estimated to have reached $5.19 billion in 2024, reflecting the increasing demand for intelligent automation. Source: AI Agents Market
Recent innovations, such as AutoGPT and BabyAGI, have introduced new levels of autonomy. AI agents leverage natural language processing (NLP) to recursively plan and execute complex tasks. Their advanced multimodal reasoning enables agents to generate insights from text, images, and code.
Meanwhile, OpenAI’s GPT-based agents continue pushing conversational AI to its limits, integrating with automated scheduling, customer support, and business analytics and tailoring interactions based on user preferences.
In this article, we will explore the core components of AI agents, their practical applications, and the key steps of building your own AI agent. We will also examine the challenges and emerging trends in autonomous intelligence.
What Is an AI Agent?
The term AI agent can be interpreted differently depending on the context. In a broad sense, an agent can refer to any system that operates autonomously based on predefined rules or adaptive learning mechanisms. However, more advanced definitions consider agents as entities exhibiting goal-directed behavior capable of reasoning, planning, and interacting with other systems.
For practical applications, an AI agent is best understood as an autonomous system that perceives its environment, processes data, makes decisions, and takes actions.
A structured framework for LLM-based AI Agents proposed by a group of scientists from SenseTime Research in 2023. Source: TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents
How AI Agents Work
AI agents function through a structured sequence of operations that includes perceiving data, processing it, and executing tasks according to the insights from the analyzed information. This is the ability to refine their behavior, making them distinct from traditional business applications. To build an AI agent, developers must create systems that perform tasks with an added layer of adaptability, allowing them to cater to user preferences and deliver more personalized responses.
Agent’s workflow example. Source: What are AI Agents?- Agents in Artificial Intelligence Explained by AWS
The workflow of an AI agent can be broken down into three core stages:
1. Goal Definition and Task Planning
AI agents require predefined goals, constraints, and tools corresponding to the task. These elements shape their behavior and decision-making process. While autonomous in execution, AI agents operate within an environment defined by:
Developers design the agent’s underlying architecture and training processes.
Deployed configurations define tool integrations, access permissions, and operating constraints.
End users who provide specific aims and contextual inputs that a particular agent must process.
To build an AI agent capable of handling complex tasks, developers enable agents to perform task decomposition, breaking high-level goals into manageable sub-tasks. Then, they can optimize their workflow by prioritizing critical actions, iterating through possible solutions, and choosing the appropriate external tools.
In simpler use cases, an agent may not require explicit planning. Instead, it may simply rely on iterative refinement and continuously improve its responses based on real-time human feedback.
2. Reasoning and Decision Execution
Once an overall plan is established, the AI agent needs to define its further actions in detail based on the available information. However, no single model has exhaustive knowledge. To address this limitation, agents utilize external resources such as:
Real-time data APIs provide updated market trends, supply chain logistics, or system monitoring insights.
Sensor networks used in robotics and industrial automation for environmental awareness.
Web search tools to gather the latest insights beyond their training data.
Specialized machine learning models, such as predictive analytics tools for risk assessment or recommendation systems.
Other AI agents specialize in domain-specific reasoning (e.g., a legal assistant consulting a contract analysis model), with different AI agents exchanging information to improve accuracy and decision-making.
The agent reassesses its strategy and tactics at each step, incorporating new data and avoiding potential errors. This self-correcting ensures that outputs remain relevant and context-aware, particularly in decision-heavy applications like automated research assistants. If new data suggests a better approach, the agent can recalculate its plan and adjust its actions accordingly.
Consider a predictive maintenance AI deployed in a factory. It may start by analyzing sensor data from industrial machines. If an anomaly is detected, it queries a failure prediction model trained on historical maintenance logs. If the risk is high, it can autonomously generate a work order for technicians or adjust machine parameters to prevent failure.
3. Learning and Continuous Improvement
AI agents constantly improve through feedback loops that refine their decision-making processes. The feedback can come from:
Human input, where users validate or correct the system’s outputs.
Agent self-evaluation, where performance metrics and past decisions are logged for future optimization.
Collaborative learning, where multiple agents share insights to improve group efficiency.
Machine learning techniques play a crucial role in this continuous improvement process. AI agents can fine-tune their decision-making, learning patterns, and behaviors that optimize future performance by analyzing past actions and their outcomes. This enables agents to adapt to changing environments and user preferences more accurately.
An Overview of an LLM-based Software Development Agents Framework consisting of three agents working cooperatively to resolve a JIRA issue. Source: Human-In-the-Loop Software Development Agents
AI Agent Categories
AI agents can be classified based on complexity, decision-making workflow, and ability to learn or collaborate.
1. Reactive Agents
Reactive agents operate solely on current input, without any strategic planning or long memory. They respond instantly according to predefined rules, however, they can be very creative with each particular output.
Examples:
Chess-playing bots like Deep Blue — IBM’s Deep Blue, which defeated a human chess champion, relied on brute-force search rather than learning from previous games.
Spam filters — Email systems classify messages as spam with rule-based filters.
Obstacle-avoiding robots — Robotic vacuum cleaners use simple sensor feedback to navigate and don’t need a map of their environment.
2. Deliberative Agents
Deliberative agents incorporate planning and use explicit reasoning to make decisions. Based on this complex analysis, they model their environment, predict possible outcomes, and choose their next action.
Examples:
Self-driving cars — Autonomous vehicles like those developed by Waymo anticipate road conditions and make informed driving decisions rather than simply avoiding obstacles.
AI bots in logistics and supply chain management — Amazon’s warehouse robots calculate optimal routes for picking and sorting packages, adjusting to real-time conditions.
AI medical diagnosis systems — Tools like IBM Watson Health analyze structured and unstructured patient data, reason through possible diagnoses, and recommend treatments based on medical literature, clinical guidelines, and patient history.
An example of non-reactive autonomous vehicle benchmarking considering the multi-modality of driving. Source: NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
3. Learning Agents
These agents improve performance over time by learning from past experiences. They use supervised, unsupervised, or reinforcement learning methods to enhance efficiency and adapt to user behavior. Machine learning algorithms allow agents to recognize patterns in data, improving their responses to new situations. Machine learning techniques also enable learning agents to refine their decision-making processes based on previous outcomes.
Examples:
Fraud detection systems — AI models analyze bank transaction patterns and adapt to new fraudulent behaviors, continuously expanding their detection capabilities.
Personalized recommendation engines — Netflix and Spotify AI dynamically refine user recommendations based on evolving preferences, often suggesting content the user may not have actively searched for.
AlphaGo and AlphaZero — DeepMind’s AI learned complex board games by playing against itself and refining strategies, ultimately surpassing human expertise.
4. Multi-Agent Systems (MAS)
MAS involves multiple AI agents interacting, cooperatively or competitively, to achieve shared or individual goals.
Examples:
Traffic management AI systems — Smart traffic lights use a network of AI agents to coordinate and optimize city traffic flows.
Swarm robotics — Drones used in search-and-rescue missions coordinate their worker to map disaster zones and find survivors.
Financial market trading bots — AI agents in stock markets analyze trends and execute trades autonomously, sometimes competing against other AIs.
A multi-agent system for intelligent transportation suggested by a group of researchers working under contract with the US Department of Energy. Source: GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility
These categories illustrate how AI agents power real-world scenarios, from basic automation to strategic decision-making.
AI Agents Use Cases
Here are some key sectors where AI agents are making an impressive impact, as shown by recent real-world examples of their implementation.
1. Healthcare: AI in Diagnostics, Patient Care, and Robotic Surgery
AI agents assist with diagnosis, monitoring, and robotic-assisted medical procedures, improving efficiency and accuracy.
Recent Examples:
Babylon Health’s AI-powered triage system—This AI agent performs well, interacting with patients, asking them questions, and suggesting diagnoses and treatments.
AI in Radiology — AI-powered agents like Qure.ai analyze X-rays and CT scans, detecting anomalies.
Surgical Robotics — Applications like da Vinci Surgical System use AI-driven robotic arms to enhance precision in minimally invasive surgeries.
Example of Qure.ai software X-Ray interpretation. Source: Radiograph accelerated detection and identification of cancer in the lung (RADICAL)
2. Finance: Fraud Detection, Algorithmic Trading, and Credit Scoring
AI agents in finance analyze patterns, detect anomalies, and make real-time trading decisions based on large-scale financial data.
Comparison of traditional credit scoring methods versus AI-driven models. Source: Innovative API Frameworks and Data-Driven Modelling for Enhanced Fintech Lending Applications
Recent Examples:
Mastercard’s AI-Powered Fraud Detection — Mastercard’s Decision Intelligence platform prevents fraudulent transactions by continuously researching user behavior and subsequently detecting unusual actions.
AI-Driven Trading — Hedge funds like Two Sigma deploy AI agents for implementing high-frequency trading strategies.
Automated Credit Risk Assessment — AI agents like Zest AI analyze non-traditional credit data to provide more inclusive loan approvals.
3. Autonomous Vehicles: AI in Perception, Planning, and Control
Self-driving vehicles rely on AI agents for environment perception, trajectory planning, and complex real-time control.
Recent Examples:
Tesla’s Full Self-Driving (FSD) Beta — AI-powered agents process real-time sensor data to navigate various roads and adjust to changing conditions.
Waymo’s Robotaxi— AI agents manage ride requests and optimize routes for driverless taxis operating in Phoenix and San Francisco.
Aurora Innovation’s Autonomous Freight Trucks — an autonomous driving system tailored for highway environments reduces fuel consumption and guarantees safety.
Robotaxi timeline Source: Robotaxi Timeline
4. Cybersecurity: AI in Threat Detection and Incident Response
Cybersecurity AI agents detect vulnerabilities, prevent attacks, and respond to threats without step-by-step human intervention.
Recent Examples:
Microsoft’s Security Copilot — This AI-driven agent helps cybersecurity teams analyze threats and recommend mitigation strategies.
Darktrace’s AI Cyber Defense — This agent-based system uses unsupervised learning to detect anomalies, predict threats, and prevent cyber attacks before they escalate.
AI-Powered Phishing Detection—Google’s Safe Browsing AI continuously scans billions of websites and emails, blocking phishing attempts in real time.
5. E-commerce: AI in Pricing, Personalization, and Customer Support
AI agents in e-commerce improve pricing strategies, prepare personalized recommendations for users, and automate customer interactions.
Recent Examples:
eBay’s AI-Powered Pricing Agent — analyzes real-time market trends and competitors’ offers to suggest optimal prices for sellers autonomously.
Meta’s AI Customer Support Agents — AI-driven assistants handle customer queries across WhatsApp, Messenger, and Instagram with minimal human intervention.
Walmart’s AI Inventory Management — predicts demand fluctuations and optimizes warehouse stocking.
6. Industrial Automation: AI in Manufacturing and Logistics
Industrial AI agents enhance efficiency, predict failures, and coordinate systems in factories and manufacturers' warehouses, driving robotic process automation to streamline operations.
Recent Examples:
Siemens AI-Powered Predictive Maintenance — AI agents analyze machine sensor data to prevent breakdowns before they happen.
Boston Dynamics’ Stretch Robot — Uses AI-driven perception and motion planning to automate warehouse package handling.
BMW’s AI-Driven Quality Control Agent — Computer vision-based AI inspects assembly line products in real time.
Key Components of an AI Agent
Modular architectures are designed to build AI agents, where each component serves a specific role in perception, reasoning, decision-making, and task execution. These components work together to ensure the agent can interact with its environment, process complex inputs, and take meaningful actions through an agent computer interface designed for seamless operation.
General workflow of an AI agent, with its perception, brain, and action mechanisms. Source: AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways
1. Perception Module
The perception module is responsible for data acquisition, enabling the AI agent to interpret its surroundings.
Input Sources
Physical Sensors — Cameras (for vision-based agents), LiDAR (for autonomous navigation), and microphones (for speech recognition software).
Software APIs & Logs — API endpoints for fetching external data (e.g., weather reports or financial market data) and system logs for monitoring.
Natural Language Processing (NLP) Pipelines — Used in text-based AI agents to extract relevant information from human input including user feedback.
Processing Methods
Signal Processing — Converts raw data into structured formats for further analysis.
Feature Extraction & Data Fusion — Merges multimodal inputs (e.g., combining audio and visual data in robotics).
2. Processing Unit
This component uses AI models and algorithms to analyze input data and generate insights.
Model Architectures
Rule-Based Systems — Simple logic-driven systems used in early AI assistants and industrial automation.
Statistical Models — Bayesian networks for probabilistic reasoning, Markov decision processes (MDPs) for sequential decision-making.
Deep Learning Models — Convolutional Neural Networks (CNNs) for vision-based tasks, Transformers for NLP and generative AI agents.
Edge vs. Cloud Computing
Edge AI Agents — Operate on local devices, enabling real-time decision-making without relying on external servers (e.g., autonomous drones processing visual data onboard ).
Cloud-Based AI Agents — Offload processing to remote servers, allowing for scalable learning and multi-agent coordination (e.g., AI-powered fraud detection aggregating insights from global banking transactions).
3. Decision-Making Engine
This component defines the agent’s following action using various computational techniques.
Approaches
Reinforcement Learning (RL) — Used in robotics, autonomous vehicles, and game AI for self-improving behavior based on rewards.
Symbolic AI & Logic-Based Planning — Applied in automated theorem proving, expert systems, and rule-based agents.
Hybrid Systems — Combining ML-based and rule-based logic (e.g., AI medical diagnosis tools using statistical models and expert-driven heuristics).
Example Use Case
High-Frequency Trading (HFT) Agents — AI-powered financial agents make split-second trading decisions using real-time market analysis and reinforcement learning.
Possible agent architectural designs using Reinforcement Learning (RL) in manufacturing. Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines
4. Actuation Mechanism
The actuation mechanism executes the action following the decision made by the agent.
Execution Pathways
Physical Actions — Industrial robots executing assembly-line tasks and autonomous drones adjusting flight paths.
Digital Actions — API-triggered responses (e.g., a cybersecurity AI blocking unauthorized access), chatbots generating text responses.
Control Methods
PID Controllers — Used in robotics for precise motion control.
End-to-End AI Control – Autonomous agents learning movement strategies via reinforcement learning.
5. Memory & Learning Component
AI agents collect and store their past experiences to refine decision-making and adapt to new data. The memory system ensures that the agent improves performance.
Types of Memory in AI Agents
Short-Term Memory (STM): Retains temporary context within a session (e.g., a virtual assistant remembering the user’s last query in an ongoing conversation).
Long-Term Memory (LTM): Stores accumulated knowledge (e.g., an AI-powered medical assistant learning from historical patient data to improve diagnostic recommendations).
Episodic Memory: Enables agents to recall specific events and adjust behavior accordingly (e.g., a trading AI recognizing financial market anomalies based on past economic crises).
AI agents leverage machine learning techniques to refine their memory systems over time, adapting to new data and improving decision-making based on past experiences.
Adaptive Learning Mechanisms:
Supervised Learning: Models improve through labeled data (e.g., fraud detection agents trained on past fraudulent transactions).
Reinforcement Learning (RL): Agents learn optimal behavior by maximizing rewards (e.g., game AI learning strategies through self-play).
Self-Supervised & Unsupervised Learning: Agents improve pattern recognition without labeled datasets (e.g., NLP models mastering language comprehension from vast text corpora).
Machine learning plays a crucial role in these adaptive processes, whether it's supervised learning, reinforcement learning, or more advanced methods like self-supervised and unsupervised learning.
Federated Learning in Adaptive AI Agents:
In privacy-sensitive domains like healthcare and finance, AI agents must learn from distributed data without centralizing user information. Federated learning enables AI agents to collaborate on model updates while keeping data decentralized.
Example: medical AI agents improve disease prediction models without transferring patient records.
6. Communication Interface
This module enables interoperability with humans and other systems.
Protocols & APIs
REST & GraphQL APIs — Used for exchanging structured data (e.g., AI-driven financial analysis tools querying stock market APIs).
WebSockets & Message Queues — For real-time data streaming (e.g., AI-powered chatbots maintaining session awareness).
Multi-Agent Communication
Agent-to-Agent Protocols — AI agents coordinating in multi-agent simulations, autonomous drone swarms, and smart city traffic management.
In multi-agent systems, tasks can be processed using fundamentally different strategies, including independent execution and sequential coordination. Source: BMW Agents — A Framework For Task Automation Through Multi-Agent Collaboration
How to Build an AI Agent
1. Define Objectives and Scope
Setting clear objectives is crucial before you start building an AI agent. You need to establish clear problem definition and success criteria relevant to your business, ensuring your product aligns with the tasks and performance metrics. That presumes considering the following factors:
Task Definition: What specific function will the agent perform? (e.g., customer support, autonomous vehicle decision-making, fraud detection).
Performance Metrics: What are the key indicators of success? (e.g., accuracy, response time, computational efficiency).
Operational Constraints: What limitations must be accounted for? (e.g., hardware resources, real-time processing requirements, regulatory compliance).
2. Select the Appropriate Model and Algorithms
The choice of AI model depends on the agent’s complexity, required adaptability, and available data.
Rule-Based Systems: Suitable for deterministic tasks with well-defined rules (e.g., automated customer service routing).
Machine Learning Models: Effective for pattern recognition and decision-making in structured data environments (e.g., decision trees for credit scoring).
Deep Learning Architectures: Required for complex unstructured data (e.g., convolutional neural networks (CNNs) for computer vision and transformers for NLP-based AI agents).
Reinforcement Learning (RL): Ideal for agents that interact dynamically with their environment and optimize decisions over time (e.g., robotic control systems or autonomous trading agents).
Hybrid Approaches: Many AI agents integrate multiple models (e.g., a chatbot using rule-based intent matching + LLM-based natural language generation).
3. Data Collection and Preparation
You'll need high-quality data to effectively build AI agents. Gather relevant datasets, including internal data, and ensure they are cleaned and structured properly.
Data Acquisition: Gathering data from APIs, transaction logs, sensors, or external datasets.
Data Preprocessing: Handling missing values, normalizing inputs, and extracting relevant features.
Data Augmentation: Creating synthetic data to improve robustness (e.g., generating adversarial examples for fraud detection AI).
Labeling and Annotation: Essential for supervised learning models (e.g., human-labeled medical images for diagnostic AI systems).
4. Design the Agent Architecture
When you build AI agents, choosing the right architecture is key. You may opt for neural networks, decision trees, or other AI methodologies, depending on your use case. Make sure the architecture is scalable and adaptable to future needs as your AI agents evolve.
Data Flow & Processing: Defining how the agent receives input, processes it, and executes actions.
Model Integration: Determining how the AI interacts with external databases, APIs, or multi-agent systems.
Decision Pipeline: Structuring the decision-making process (e.g., multi-stage filtering, hierarchical reasoning).
Deployment Considerations:
Edge AI: Low-latency decision-making (e.g., industrial robotics).
Cloud AI: Scalable, centralized processing (e.g., AI-powered customer analytics).
Hybrid AI: Balancing real-time edge inference with cloud-based retraining (e.g., AI in autonomous drones).
5. Training and Testing
Training involves optimizing AI models using large-scale datasets and iterative learning cycles.
Supervised Learning: For agents requiring labeled training data (e.g., facial recognition in security applications).
Unsupervised Learning: For clustering and anomaly detection (e.g., AI detecting fraudulent transactions).
Reinforcement Learning: For agents that learn by interacting with environments (e.g., robotics and dynamic pricing strategies).
Performance Evaluation:
For Classification Tasks: Accuracy, precision-recall curves, and F1-score.
For Regression Tasks: Mean Squared Error (MSE) and R-squared.
For Real-Time Systems: Latency and response time.
For Adaptive Agents: Long-term reward optimization (e.g., AI in strategic gaming).
6. Deployment and Monitoring
The AI agent’s deployment should ensure it can effectively manage real-time user interactions, especially in high-traffic environments.
Deployment Strategies:
Containerized Deployment: Using Docker, Kubernetes for scalable cloud-based AI.
On-Device Deployment: Deploying models on edge devices (e.g., TensorFlow Lite for mobile AI).
Hybrid Deployment: Combining cloud-based inference with local execution (e.g., AI in autonomous industrial systems).
Monitoring & Maintenance:
Model Drift Detection: Identifying performance degradation as data distributions change.
Audit Logging: Tracking AI decisions for transparency and compliance (e.g., financial AI risk monitoring).
Continuous Retraining Pipelines: Automating model updates based on new data (e.g., reinforcement learning in recommendation systems).
Challenges and Trends in AI Agents
Key Challenges
Data Quality & Bias — AI agents in criminal justice risk assessment have shown racial bias due to biased historical data, leading to unfair sentencing predictions.
Interpretability — In AI-driven medical diagnosis, deep learning agents may provide accurate recommendations, but their lack of explainability makes them difficult to trust without human validation.
Scalability — AI customer service agents handling high-volume e-commerce queries must adapt dynamically to traffic spikes, requiring efficient cloud scaling strategies.
Security Risks — Adversarial attacks on self-driving cars have demonstrated that subtle image perturbations can fool AI into misclassifying road signs, posing safety risks.
Emerging Trends
Multi-Modal AI Agents — AI agents in autonomous retail checkout systems process visual, speech, and behavioral data for real-time fraud prevention.
Autonomous AI Systems — Self-improving AI agents in automated software debugging analyze and patch vulnerabilities without human intervention.
Edge AI Deployment – AI agents in smart factories optimize production processes on-device, reducing cloud dependency and minimizing response time.
Federated Learning for Privacy — AI in personalized healthcare leverages federated learning to improve predictive diagnostics across hospitals while keeping patient data decentralized.
Synthetic Data for AI Training — AI agents in self-driving simulation environments generate realistic synthetic road scenarios, improving navigation models without real-world testing risks.
Final Thoughts: Why Create AI Agents
AI agents transform industries through automation, informed and explainable decision-making, and human-AI collaboration. However, to build your own AI agent, you must carefully consider model selection, data quality, architecture design, and ethical risks.
As AI technology evolves, AI agents' capabilities will expand, allowing them to exhibit greater autonomy and integrate more seamlessly into everyday life. However, agent development must remain focused on ensuring that AI agents make high-quality decisions that complement human expertise, not solely replacing human labor for efficiency's sake.
