Automation & Workflow Definitions

A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.

A meta-framework for composing multi-step, tool-using agents with routing, memory, and validation built in.

An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.

A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.

A user interface layer that surfaces agent actions, context, and controls so humans can supervise, steer, or approve runs.

An auto-generated outline with headings, keywords, and intent that guides humans or agents to produce search-optimized content.

Designing automations to interact with systems through stable APIs instead of screens or exports.

A commitment for how quickly and reliably an automation responds or completes.

An agent that navigates sitemaps or URL lists, collects content and metadata, and feeds audits or refresh workflows.

A task that reprocesses historical events or data to close gaps after downtime or schema changes.

A configurable layer that evaluates conditions (if/then logic, scoring, routing) without redeploying code.

An HTML tag that signals the authoritative URL for a page to prevent duplicate content from diluting rankings.

Streaming inserts, updates, and deletes from a database so downstream systems stay in sync in near real time.

A numeric signal indicating how certain a model or rule is about a prediction, often used to decide when to escalate to a human.

A holding area for messages that keep failing so they can be inspected, fixed, and replayed safely.

Parsing unstructured text to pull out structured values like vendor names, amounts, dates, or ticket priorities.

A feedback step where model outputs are scored against rules or heuristics before the workflow proceeds, common in agentic systems.

A predefined fallback route that keeps workflows in a safe state when a dependency or model misbehaves.

Structured model outputs that invoke predefined functions/tools with typed arguments, enabling safe actions and structured data extraction.

The authoritative data source or label set used to evaluate model accuracy and monitor drift over time.

LLM outputs constrained by schemas, validations, or allow/deny lists to keep responses safe and on-brand.

Constraints that keep model outputs and automation steps inside business rules: allow/deny lists, JSON schemas, tone controls, or PII redaction.

A checkpoint where a person reviews, approves, or corrects an automated decision before it moves forward.

Designing actions so they can run multiple times without creating duplicates or inconsistent state.

The maximum number of workflow runs allowed in parallel to protect downstream systems or licenses.

Grouping related search queries using semantic similarity so content can target intent without cannibalizing rankings.

An immediate toggle that halts an automation or feature without redeploying code.

A framework for building LLM apps with primitives for prompts, tools, memory, and chains/graphs.

Granting automations only the permissions required to run specific workflows.

A reusable prompt scaffold with variables and instructions that standardizes how AI agents perform tasks across workflows.

Model Context Protocol (MCP) standardizes how AI apps expose tools, data sources, and capabilities via a uniform interface.

A buffer that decouples producers and consumers so bursts of work are processed reliably without dropping events.

An open-source workflow automation platform with a visual editor, nodes for APIs/apps, and self-hosting support.

Collecting logs, metrics, and traces to understand how workflows run, where they fail, and how long they take.

A documented instruction for an automation with owner, inputs, expected output, and rollback notes.

A templated, data-driven page generated at scale (locations, SKUs, comparisons) and kept fresh via automation.

The number of items waiting in a queue; signals whether capacity is keeping up with demand.

Combining a prompt with fresh context retrieved from a knowledge source so the model responds with grounded, specific answers.

Automatically re-running failed tasks after increasing delays to avoid hammering a flaky downstream service.

Logic that selects which model, prompt, or tool to run based on inputs like task type, language, or complexity.

Codifying a manual operational checklist into repeatable steps with clear inputs, owners, and rollback paths.

Structured data (JSON-LD) added to webpages to help search engines understand entities and unlock rich result eligibility.

Automatically labeling queries (informational, navigational, transactional, commercial) to guide page structure and conversion strategy.

Retrieval that matches meaning using embeddings instead of exact keywords; foundational for modern RAG and site search.

An automated tool or agent that collects search result data—titles, snippets, FAQs—to inform briefs, gap analysis, and on-page improvements.

A target for reliability or speed (for example, 99% of tickets triaged in under 5 minutes) that automation must meet.

When data in source and target systems diverge after syncs or partial failures.

Intentionally slowing or spacing requests to stay within rate limits or avoid overwhelming a downstream system.

A signal that starts an automated flow: a form submit, status change, schedule, or inbound webhook.

A write that updates a record if it exists or inserts it if it does not.

A store for embeddings that allows semantic search, powering similarity lookups for prompts or recommendations.

Tracking iterations of an automation with release identifiers so you can audit, rollback, or run experiments safely.

An HTTP callback sent from one app to another in real time when an event occurs, often used as the entry point to automations.

A quality checkpoint that scores automation or AI outputs against schemas, policies, or heuristics before finalizing.

Coordinating multi-step tasks across systems with triggers, dependencies, and clear success or failure paths.

A fully automated activation flow that provisions access, collects data, and triggers training without human intervention.