Learn to build powerful AI agents for specific tasks
A comprehensive reference of terminology used in AI agent development
An AI agent is a system that perceives its environment through inputs, makes decisions based on those inputs, and acts to achieve specific goals. In the context of modern AI development, agents often utilize large language models (LLMs) or other AI components to process information and determine actions.
A customer service agent that can access product databases, interpret customer inquiries, and provide relevant responses while following company policies.
The set of all possible actions an agent can take within its environment. In AI agent development, this typically refers to the range of functions, API calls, or outputs that an agent can produce.
A research agent might have an action space that includes: searching web APIs, querying databases, summarizing documents, and generating reports.
An open-source framework for building autonomous AI agents that can break down complex goals into subtasks and execute them using language models. AutoGPT implements recursive self-improvement mechanisms to refine its approach based on results.
Using AutoGPT to create a market research agent that can gather information from multiple sources, analyze trends, and generate comprehensive reports with minimal human supervision.
An AI agent capable of operating independently without continuous human intervention, making decisions and taking actions to achieve its goals. Autonomous agents typically incorporate planning, memory, and self-evaluation components.
A social media management agent that schedules posts, responds to comments, analyzes engagement metrics, and adjusts strategy without requiring human approval for each action.
An open-source task management system that uses language models to create, prioritize, and execute tasks based on the outcomes of previous tasks. BabyAGI implements a simple but effective task-driven autonomous agent architecture.
Using BabyAGI to manage a research project where it progressively explores a topic, identifies knowledge gaps, and creates new research tasks to fill those gaps.
A prompting technique that encourages language models to break down complex reasoning tasks into step-by-step thought processes before providing a final answer. CoT significantly improves performance on tasks requiring logical reasoning, multi-step computation, or careful analysis.
Instead of directly asking "What's the sum of the squares of 13 and 14?", a CoT prompt would guide the model: "Let's calculate this step by step. First, I'll find the square of 13, which is 13×13 = 169. Then I'll find the square of 14, which is 14×14 = 196. Finally, I'll add these results: 169 + 196 = 365."
The maximum amount of text (measured in tokens) that a language model can process at once. This includes both the input prompt and the generated output. The context window represents a fundamental limitation of current language models and influences agent design.
GPT-4 has a context window of 32,000 tokens (approximately 24,000 words), while Claude 3 Opus has a context window of about 200,000 tokens. When building an agent to analyze long documents, these limitations determine how much text can be processed in a single operation.
A framework for building sophisticated multi-agent systems where each agent has a specialized role, and the agents collaborate to accomplish complex tasks. CrewAI facilitates structured communication between agents and manages workflow organization.
Creating a content production system with a researcher agent that gathers information, a writer agent that drafts content, an editor agent that refines the writing, and a publisher agent that formats and distributes the final content.
The process by which an AI agent selects an action or response based on available information, goals, and constraints. Effective decision making is central to agent performance and typically involves evaluating options, predicting outcomes, and selecting the most promising approach.
A customer service agent deciding whether to offer a refund, replacement, or alternative solution based on the customer's history, the nature of the issue, and company policies.
Dense vector representations of text, images, or other data that capture semantic meaning in a way that allows for efficient search, comparison, and retrieval. Embeddings are fundamental to many advanced agent capabilities, especially knowledge retrieval systems.
A document retrieval system converts all documents into embeddings, then finds the most relevant documents by calculating the similarity between the query embedding and the document embeddings.
A technique where a model is given a few examples of a task before being asked to perform similar tasks. In agent development, few-shot learning is often implemented as in-context examples that guide the model's outputs to match a desired format or approach.
Providing a language model with three examples of properly formatted customer service responses before asking it to handle a new customer inquiry in the same style.
A capability of advanced language models that allows them to generate structured outputs suitable for calling external functions or APIs. Function calling enables more reliable tool use by providing a clear interface between natural language processing and programmatic actions.
An agent analyzing weather data might use function calling to generate a properly formatted query to a weather API: {"location": "San Francisco", "date": "2025-04-11", "metrics": ["temperature", "precipitation", "wind_speed"]}
Mechanisms implemented to ensure AI agents operate within defined boundaries, preventing harmful, unethical, or undesired behaviors. Guardrails may include content filtering, input validation, output verification, and safety-oriented prompt engineering.
A content generation agent might have guardrails that prevent it from creating violent content, verify factual claims against a trusted database, and limit the types of files it can access.
The tendency of language models to generate content that is factually incorrect, made-up, or ungrounded in the provided context. Hallucinations are a significant challenge in AI agent development, particularly for applications requiring factual accuracy.
A research agent might hallucinate by citing non-existent papers, inventing statistics, or creating plausible but incorrect explanations when it doesn't know the answer to a question.
The ability of language models to adapt to new tasks based on examples or instructions provided in the prompt, without updating model weights. In-context learning is a key technique for tailoring agent behavior without fine-tuning.
Teaching an agent to classify customer feedback into categories by showing it several examples of properly classified feedback in the prompt, then asking it to classify new examples.
A specialized output format available in some language models that ensures responses are formatted as valid JSON objects. JSON mode is particularly useful for agent development as it allows for structured, parseable outputs that can be directly used by other systems.
Enabling JSON mode when asking an agent to analyze sentiment in customer reviews, resulting in a structured output like
{
"overall_sentiment": "positive",
"specific_aspects": {
"product_quality": "positive",
"shipping_speed": "negative",
"customer_service": "neutral"
},
"key_points": [
"Product exceeded expectations",
"Shipping took longer than promised"
]
}
The process by which an AI agent accesses specific information relevant to a task or query. Knowledge retrieval systems typically involve vector databases, search algorithms, and relevance ranking to identify and extract the most useful information.
A technical support agent using knowledge retrieval to find relevant documentation, previous similar issues, and potential solutions when presented with a customer's problem.
A popular open-source framework for developing applications powered by language models. LangChain provides components for working with language models, embedding models, document processing, memories, agents, and various tools. It emphasizes composability and standardized interfaces.
Using LangChain to build a research agent that can retrieve information from multiple sources, process documents, maintain conversation history, and produce comprehensive reports.
A type of AI model trained on vast amounts of text data that can generate human-like text, understand context, follow instructions, and perform various language-based tasks. LLMs serve as the foundation for most modern AI agents, providing capabilities such as reasoning, generation, and comprehension.
GPT-4, Claude, Gemini, and Llama are examples of large language models that can be used as the core of AI agent systems.
A data framework designed to connect custom data sources to large language models. LlamaIndex (formerly GPT Index) specializes in creating, maintaining, and querying indexes of structured and unstructured data, making it particularly well-suited for knowledge-intensive applications.
Using LlamaIndex to build a legal research agent that can ingest, index, and query large collections of case law, contracts, and legal opinions to provide relevant information for legal questions.
A system that allows AI agents to store and retrieve information across multiple interactions or processing steps. Memory components can include conversation history, key facts, user preferences, and intermediate results.
A customer service agent using memory to recall that a customer previously mentioned having a premium subscription, allowing it to offer appropriate service options without requiring the customer to repeat this information.
A system where multiple AI agents work together, often with specialized roles, to accomplish complex tasks. Multi-agent systems typically involve structured communication protocols, role assignments, and coordination mechanisms.
A content creation system with specialized agents for research, outlining, writing, editing, fact-checking, and publishing, each performing its role and passing results to the next agent in the workflow.
The process of coordinating multiple components, agents, or services to work together effectively. Orchestration typically involves managing workflows, handling errors, scheduling tasks, and ensuring proper communication between components.
An e-commerce assistant that orchestrates interactions between a product search service, inventory checking system, recommendation engine, and order processing service to help customers find and purchase products.
The process by which an AI agent formulates a sequence of actions to achieve a goal. Planning typically involves breaking down complex tasks into subtasks, considering dependencies, and establishing an execution order.
A research agent creating a plan to investigate a topic by first defining key questions, then identifying information sources, gathering relevant data, analyzing findings, and finally synthesizing results into a comprehensive report.
The practice of designing, refining, and optimizing inputs to language models to guide them toward desired outputs. Prompt engineering is a key skill in AI agent development, encompassing techniques such as few-shot learning, chain-of-thought prompting, and system role definition.
Creating a carefully structured prompt for a customer service agent that includes the company's tone guidelines, specific product information, examples of good responses, and instructions for handling difficult situations.
An approach that enhances language model outputs by first retrieving relevant information from external knowledge sources, then using that information to generate more accurate, informed responses. RAG combines the strengths of retrieval-based and generative approaches.
A technical support agent using RAG to retrieve relevant sections from product manuals and troubleshooting guides before generating a specific, accurate solution to a customer's technical problem.
A framework that interleaves reasoning and action steps, allowing agents to plan, execute, observe, and refine their approach. ReAct prompts typically follow a "Thought, Action, Observation" cycle that makes decision-making explicit and verifiable.
A research agent using ReAct to solve a complex problem: "Thought: I need to find recent climate data. Action: Search for 'latest IPCC climate report'. Observation: Found the IPCC Sixth Assessment Report. Thought: Now I need specific information on sea level rise..."
The ability of an AI agent to evaluate its own outputs, decisions, or reasoning processes, and make improvements based on this evaluation. Self-reflection enables agents to catch errors, refine strategies, and improve performance over time.
A writing assistant that generates a draft, then critically reviews its own work by evaluating clarity, coherence, accuracy, and style before producing an improved version based on its self-critique.
A set of instructions or context provided to a language model that persists across the entire interaction, establishing the model's role, constraints, and capabilities. The system prompt serves as a foundation for the agent's behavior and is typically not visible to end users.
A system prompt for a financial advisor agent might include instructions to follow regulatory guidelines, avoid making specific investment recommendations, explain concepts clearly, and maintain a professional tone throughout the conversation.
The process of breaking down complex tasks into simpler, manageable subtasks that can be addressed individually. Task decomposition is essential for planning and tackling problems that are too large or complex to solve in a single step.
An agent decomposing the task "create a market analysis report" into subtasks like "identify key competitors," "gather market size data," "analyze pricing trends," "identify customer segments," and "summarize findings and recommendations."
The ability of an AI agent to interact with and utilize external functions, APIs, or services to accomplish tasks beyond its native capabilities. Tool use significantly expands what an agent can do by connecting it to specialized systems and data sources.
A travel planning agent using tools to search flight databases, check hotel availability, look up weather forecasts, and access mapping services to create a comprehensive travel itinerary.
A specialized database designed to store and efficiently query vector embeddings. Vector databases enable semantic search by finding entries that are conceptually similar rather than just matching keywords, making them essential components for knowledge retrieval in AI agents.
Storing thousands of product description embeddings in a vector database to allow a shopping assistant to find semantically similar products when a customer describes what they're looking for in natural language.
A defined sequence of steps, processes, or operations that an agent follows to complete a task. Workflows provide structure to agent behavior and can incorporate decision points, loops, and parallel processes.
A content moderation agent using a workflow that first classifies content type, then applies specific moderation rules based on the classification, flags potential violations, reviews edge cases, and finally takes appropriate action.
A human-readable data serialization format often used to configure AI agent behaviors, tool sets, workflows, and other aspects of agent systems. YAML configuration allows for easy modification of agent parameters without changing code.
Using a YAML file to define a research agent's tools, API keys, memory configuration, maximum token usage, and response formatting preferences.
The ability of language models to perform tasks without being given specific examples first. In agent development, zero-shot learning refers to providing instructions for a new task without accompanying examples, relying on the model's pre-trained capabilities to understand and execute the task.
Asking an agent to "Analyze this customer feedback and categorize the sentiment as positive, negative, or neutral" without providing examples of how to categorize sentiment, relying on the model's pre-existing understanding of sentiment analysis.