3. Generative & Agentic AI

An agent framework provides the architectural foundation and tooling needed to build, orchestrate, and manage autonomous AI agents capable of performing complex, multi-step tasks. It supports core functions such as planning, tool use, memory, state management, coordination between multiple agents, and execution monitoring, enabling scalable and reliable agent-driven workflows.

Agentic workflow automation uses autonomous AI agents to manage and execute complex business processes end-to-end. These agents coordinate tasks, make decisions, and adapt dynamically to new data, conditions, or requirements, enabling flexible, scalable, and continuously improving automation across an organization.

Chain of Thought (CoT) reasoning is a method that enables AI models to generate step-by-step logical reasoning when solving problems, rather than providing only final answers.

A context window is the amount of input information — measured in tokens — that a large language model can process and consider at one time when generating a response. It sets the limit for how much text, history, or conversational context the model can use to understand prompts and produce coherent, accurate outputs.

Few-shot and zero-shot learning are techniques that leverage a model's pre-trained representations to perform new tasks with minimal or no task-specific examples. In few-shot learning, the model adapts to a new task using only a small set of labeled instances, often by conditioning on example–response pairs within the prompt or through lightweight parameter updates. In zero-shot learning, the model uses its generalized pre-trained knowledge to execute a task based solely on instructions, without any direct examples, relying on semantic understanding and transfer capabilities built during large-scale training.

A foundation model is a large-scale AI model trained on broad, diverse datasets that captures general patterns across language, images, code, or other modalities. Because of its scale and versatility, it can be adapted — through fine-tuning, prompt engineering, or other techniques — to perform a wide range of downstream tasks such as text generation, image synthesis, classification, or code completion. Foundation models serve as the base layer for many modern AI applications.

The memory layer in AI systems stores contextual information from past interactions, enabling the model to "remember" past inputs and improve its responses over time.

Model Context Protocol (MCP) is a new standard that enables AI models, particularly large language models (LLMs), to securely connect to external tools, data, and applications in a structured manner. Instead of relying solely on training data or a single prompt, MCP provides a formal method for models to access context, perform actions, and interact with enterprise systems, ensuring governance and traceability.

In practice, MCP defines how context is shared between models and other systems. It standardizes the discovery, use, security, and tracking of tools, allowing AI agents to operate reliably in real-world software environments.

An orchestrator agent coordinates multiple AI models or sub-agents by managing workflows, dependencies, and decision-making across the system. It determines which agent or model should handle each step, oversees the flow of information between them, resolves conflicts or ambiguities, and ensures the overall process executes efficiently and coherently.

Prompt engineering is the art and science of crafting input prompts that effectively guide generative AI models to produce precise, relevant, and consistent outputs. It involves structuring language, instructions, and context in ways that optimize how the model interprets the request and performs the desired task.

Self-improving agents are AI systems capable of autonomously refining their strategies, prompts, workflows, or underlying models based on feedback, performance outcomes, or changes in their environment. They continuously evaluate their own behavior, identify weaknesses, and adapt their decision-making processes to improve effectiveness over time without requiring explicit human intervention.

Tool use refers to an AI agent's capability to interact with external systems, such as APIs, databases, calculators, search engines, or specialized reasoning modules, to perform tasks that extend beyond text generation. By integrating these tools into its workflow, the agent can access real-time information, execute complex operations, and produce more accurate and actionable results.

Vector databases store and retrieve data as mathematical embeddings—numerical representations of meaning—enabling semantic search, similarity matching, and contextual recall for AI systems.