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AI model integration connects your existing application to an AI model through an API, so your software can understand language, search your own data, and take action. Models can run in the cloud (OpenAI, Gemini, Anthropic, Mistral) or offline (Ollama). With Adobe ColdFusion, you can add it without a rewrite.
Adobe has released ColdFusion 2025 Update 11 and ColdFusion 2023 Update 22. The releases resolve critical, important, and moderate vulnerabilities that could lead to arbitrary code execution, privilege escalation, arbitrary file system read, and security feature bypass.View the security bulletin, APSB26-82, for more information. Download the updates ColdFusion 2025 updates ColdFusion 2023 updates What’s newThis update introduces the flag -Dcoldfusion.sql.allowUnsafeTableIdentifiers. The default is False. Updates to Performance Monitoring Toolset for both CF 2025 and CF 2023. Updated add-on installers Bug fixes View […]
How to validate, block, redact, and safely fail AI inputs and outputs before your helpful assistant becomes a compliance incident with a loading spinner.
A quick reference for the core ColdFusion AI concepts, when to use them, and what to watch for.
Part 1 of an 8 part series on how to integrate your ColdFusion application with Slack to create a two way chat portal.
Part 2 of an 8 part series of building a chat gateway between Slack and ColdFusion.
Nobody admires a well-designed database—until your chat system starts mixing up customer conversations. Let’s build a data model your future self won’t hate.
Adobe has released ColdFusion 2025 Update 10 and ColdFusion 2023 Update 21. The releases introduce some important advancements that reinforce a clear direction: secure-by-default behavior, tighter control, and better developer governance. Find this and other updates here: ColdFusion 2025 updates ColdFusion 2023 updates This update focuses heavily on addressing critical security concerns while also giving developers more control over how their applications interact with external systems, attachments, and XML processing. Security fixes and cumulative patching Both updates address multiple vulnerabilities that could result in […]
This article moves beyond simple, stateless ChatModel() calls and introduces Agent() as the layer that gives ColdFusion AI conversational memory. It explains how message windows, token windows, PERUSER, memory keys, and persistent stores help an assistant remember recent context without accidentally sharing one user’s conversation with another. It also separates short-term chat memory from durable user preferences, emphasizing that important preferences belong in your application data, not in a rolling memory window. The core lesson is that memory makes an AI assistant feel more coherent, but ColdFusion still controls identity, persistence, validation, privacy, and truth.
This article introduces MCP, or Model Context Protocol, as the next layer in building more capable ColdFusion AI applications. After using ChatModel() for stateless prompts, Agent() for memory, and CFC tools for local application capabilities, MCP provides a standardized way for AI workflows to connect with tools, prompts, and resources across system boundaries. The article explains the difference between MCP hosts, clients, and servers; when to use MCP instead of local CFC tools; how ColdFusion can consume external MCP servers or expose its own capabilities; and why security, authorization, observability, and careful tool design still matter. The core lesson is that MCP gives your AI assistant a passport, but ColdFusion still decides where it is allowed to go.
This article introduces RAG, or Retrieval-Augmented Generation, as the layer that lets ColdFusion AI answer questions using your own documents instead of relying only on model knowledge, memory, or guesses. It explains how ColdFusion can load documents, split them into chunks, create embeddings, store them in a vector store, retrieve relevant content, and use that context to generate grounded answers. The article also covers simpleRAG(), ask() versus chat(), chunking, minScore, maxResults, ingestion timing, persistent vector stores, permissions, stale content, and source quality. The core lesson is that RAG gives the robot an open-book test, but ColdFusion still decides which book it is allowed to read.
This article introduces CFC tools as the next layer in building useful AI features with ColdFusion. After covering stateless ChatModel() calls and memory-enabled Agent() conversations, we now give the assistant a controlled way to request real application capabilities through ColdFusion components. The article explains how to expose specific CFC methods as tools, write useful tool descriptions, inspect toolExecutionRequests, validate model-generated arguments, enforce authorization inside the tool, handle read versus write actions, log tool usage, and keep the application firmly in control. The core lesson is simple: tools let the AI ask for real data or actions, but ColdFusion validates, authorizes, executes, and decides what happens next.
