Glossary
Plain-English definitions of the terms you will meet when evaluating AI agents, automation, and modern software, each with a note on how it applies in a real business.
An AI agent is a software system that uses a large language model to plan and carry out multi-step tasks, calling external tools such as CRMs, databases, email, or APIs to reach a goal. Unlike a chatbot, which only replies to messages, an agent takes actions and can decide what to do next based on results.
In practice: Pixetech builds agents with confidence thresholds, audit logs, and human handoff so uncertain cases are escalated rather than guessed.
AI agent developmentAgentic AI refers to AI systems designed to operate with a degree of autonomy: setting sub-goals, choosing tools, executing steps, checking results, and retrying or escalating when something fails. It is the architectural approach behind AI agents and multi-agent workflows.
Agentic AI developmentAI automation is the use of machine learning and language models to perform business tasks that previously required human judgment, such as reading documents, qualifying leads, triaging support tickets, or drafting responses. It differs from rule-based automation (RPA) because it handles unstructured input and variation.
In practice: Typical first projects: lead follow-up, invoice and document processing, support triage, and internal reporting.
AI business automationA multi-agent system coordinates several specialised AI agents, each responsible for one part of a workflow (for example research, drafting, review, and execution), with an orchestrator that routes work between them. It improves reliability by keeping each agent's job narrow and testable.
Multi-agent workflowsRetrieval-Augmented Generation (RAG) is a technique where an AI model first retrieves relevant documents from a company's own knowledge base, then uses them to generate an answer. It grounds responses in approved sources, reduces hallucination, and lets the model answer about private or recent information it was never trained on.
AI integration servicesA large language model (LLM) is a neural network trained on very large amounts of text to understand and generate human language. Models such as GPT, Claude, and Gemini power chat assistants, agents, document extraction, and code generation.
Tool calling is the mechanism that lets a language model invoke external functions, such as 'look up this customer in the CRM' or 'create a calendar event', by producing a structured request that the application executes. It is what turns a chat model into an agent that can act.
Connecting AI to your systemsHuman-in-the-loop (HITL) is a design pattern where an AI system pauses for human review or approval before taking high-impact actions such as sending a contract, issuing a refund, or changing production data. It is a core control in AI governance.
AI security & governancePrompt injection is an attack in which malicious instructions are hidden inside content an AI system reads (an email, web page, or document) to make it ignore its original instructions or leak data. Defences include input isolation, least-privilege tool access, and adversarial testing before launch.
Adversarial testingAn AI copilot is an assistant embedded in a product or workflow that helps a human do their job faster, suggesting actions, drafting content, or summarising information, while the person stays in control of the final decision. Copilots sit between simple chatbots and fully autonomous agents.
AI SaaS & product developmentAn AI receptionist is a voice or chat agent that answers inbound calls and messages around the clock, books appointments, answers policy questions from approved content, and routes anything it cannot handle to a named person. It is common in healthcare, real estate, legal, and professional services.
Industry solutionsLegacy software modernization is the phased process of moving an aging application to maintainable, cloud-ready architecture while preserving the business rules it encodes, typically by adding tests and APIs around the old system and migrating it piece by piece so production never stops.
Modernization servicesAI cost optimization is the practice of reducing spend on model tokens, inference, and infrastructure while maintaining output quality, using techniques such as model routing, prompt compression, caching, batching, and tighter agent loops. Savings of 20–50% are common in unoptimised production systems.
Cost & performance optimizationAnswer Engine Optimization (AEO) and Generative Engine Optimization (GEO) are the practices of structuring website content so AI-powered search (Google AI Overviews, ChatGPT, Perplexity) can extract, cite, and summarise it accurately: clear definitions, FAQ and schema markup, direct answers under headings, and machine-readable summaries such as llms.txt.
Tell us the business problem. We will tell you honestly whether an agent, a workflow bot, or plain software is the right answer.