Reference
Glossary
Plain-language definitions of the terms we use across agentic AI and legacy modernization — so nothing on this site is a black box.
Back to home- Agentic AI / AI agent
- Software that uses an LLM to plan and take actions through tools — reading, deciding, and acting on real systems — rather than just generating text.MCP connectors
- AI DevOps / AIOps
- Applying AI across the delivery pipeline: intelligent CI/CD, automated code review, and AI-driven monitoring, alerting, and incident triage.DevOps
- Characterization tests
- Tests that capture a legacy system’s current behavior so you can refactor confidently without changing what it does.DevOps
- CI/CD
- Continuous Integration / Continuous Delivery — the automated pipeline that builds, tests, and ships code changes safely and repeatably.DevOps
- Evals
- Repeatable tests that measure an AI system’s quality on real tasks, so changes can be judged objectively instead of by gut feel.DevOps
- Fine-tuning
- Further-training an open model on your own data and tasks so it performs better on your specific workload.Local LLMs
- Guardrails
- Constraints that keep agents safe: permissions, allowed actions, input/output validation, and limits on what a model can do.Services
- Human-in-the-loop (HITL)
- Workflows where a person reviews or approves consequential agent actions before they run.Services
- Infrastructure as Code (IaC)
- Managing servers and infrastructure through version-controlled code instead of manual setup, for reproducible environments.DevOps
- Inference runtime
- The server software (e.g. Ollama, vLLM) that actually runs a model and serves its responses.Local LLMs
- Legacy migration
- Modernizing decades-old codebases (COBOL, Java monoliths, PHP, .NET) into clean, testable, agent-ready services — without disrupting production.Process
- LLM (Large Language Model)
- The AI model (e.g. Claude, Gemma, Qwen) that powers language understanding and generation. It can run in the cloud or locally.Local LLMs
- MCP (Model Context Protocol)
- An open standard for connecting AI agents to real systems, exposing your apps, databases, and APIs as typed, permissioned tools any compliant agent can call.MCP connectors
- MCP connector
- A server that wraps an existing system in MCP, giving agents a safe, typed, permissioned interface to read from and act on it.MCP connectors
- Observability
- Tracing, logging, and metrics that make an AI system’s behavior visible, debuggable, and auditable in production.DevOps
- On-prem / air-gapped
- Running systems on infrastructure you control. Air-gapped means fully isolated from the public internet for maximum data security.Local LLMs
- Open-weight / local model
- A model whose weights you can download and run on your own hardware (Gemma, Qwen, Nemotron, Llama…), keeping data in-house.Local LLMs
- Orchestration
- Coordinating multiple agents, tools, and steps into a reliable workflow — controlling sequencing, retries, and hand-offs.Services
- Quantization
- Compressing a model to lower numerical precision so it runs faster and on smaller hardware, with minimal quality loss.Local LLMs
- RAG (Retrieval-Augmented Generation)
- Giving a model relevant, retrieved context (documents, data) at query time so answers are grounded and citable.Services
- Rollback
- The ability to quickly revert an AI-driven change or deployment when something goes wrong.DevOps
- TDD (Test-Driven Development)
- Writing tests before or alongside code so behavior is specified and protected — the safety net that makes automated and agentic changes trustworthy.DevOps