Plain-language definitions for AI agents, MCP, iPaaS, orchestration, and governance.

Tray.ai's governance layer for MCP — deploy managed MCP Servers, expose connectors as governed agent tools, and audit every agent action.

AI systems that don't just generate responses but take actions — planning, using tools, calling APIs, and completing multi-step tasks autonomously toward a goal.

Automation that uses AI agents to handle tasks requiring judgment — not just fixed steps, but decisions, tool use, and adaptation based on what the agent finds.

An AI system that can reason about a task, use tools to act on it, and learn from feedback — not just generate text.

How multiple AI agents are coordinated to work as a system: which agent acts, when, on what data, and with what oversight.

Built with AI as a core architectural concern from day one — not bolted on as a feature after the fact.

The tools and processes for publishing, securing, monitoring, and versioning APIs — so teams can expose data and services without building custom infrastructure for every integration.

A data integration technique that tracks and streams changes in a database in real time — inserts, updates, and deletes — rather than re-pulling the full dataset on a schedule.

An architecture where independent, reusable pieces combine freely — so new capability comes from recombination, not rebuilding.

A pre-built integration between Tray.ai and a specific application — Salesforce, Slack, Snowflake, 700+ others.

The practice of moving, normalizing, and synchronizing data between systems — so one application's data is usable in another.

Using software to execute business processes automatically — at the scale, reliability, and governance that enterprise operations require.

Extract, Transform, Load — the process of pulling data from source systems, reshaping it, and loading it into a destination like a data warehouse.

The controls that make automation, integration, and AI agents safe to run in production — audit, access, approvals, compliance.

A design pattern where a human approval, review, or decision is embedded as a step in an otherwise automated process — keeping people accountable for the decisions that matter.

A Gartner-coined term for the disciplined, enterprise-wide application of automation — combining AI, integration, and process tools to automate everything that can be automated.

AI-powered extraction of structured data from unstructured documents — invoices, contracts, forms, receipts — so the data can flow directly into downstream systems without manual entry.

Integration Platform as a Service — a cloud-hosted platform for building, running, and managing integrations between applications.

A development approach where software is built primarily through visual interfaces and configuration rather than hand-written code — accessible to non-developers, extensible by developers.

Model Context Protocol — the emerging standard for how AI agents discover and call tools, data, and services.

Tray.ai's no-code / low-code platform for building AI agents that reason, act, and learn — with guardrails and audit baked in.

Coordinating data, integrations, automations, and AI agents as one system — not just moving data between apps.

Using workflows to execute business processes — onboarding, approvals, order-to-cash, incident response — without human-in-the-loop for routine steps.

Tray.ai's unit of workflow execution — a discrete run that handles one trigger through the workflow's logic to completion.

A sequence of steps that runs when a trigger fires — the unit of automation that moves data, calls systems, and orchestrates outcomes.

Software that executes a defined sequence of steps automatically — moving data, triggering actions, and routing decisions without manual intervention.