Insights on building and operating production AI agents.
Thought Leadership
Today we're launching FireFoundry, the enterprise platform for building, deploying, and operating production AI agents. Learn about our vision for making AI agents reliable, observable, and ready for the real world.
FireFoundry Team
Product
A comparison with no checkmarks in it. What FireFoundry, Microsoft Foundry, LangChain, Google ADK, Langfuse and OpenRouter each manage — and what their adopter still has to build.
Most AI platforms can host your application. Far fewer can model it. The difference is whether the enterprise has something it can own, promote, and retire.
Four DSLs, one bundle, no TypeScript. The real BundleML, AgentML and BotML behind a content-analysis agent running on FireFoundry — and where you drop back to code.
MCP made it easy for any agent to reach any tool. That is the feature and the problem. A gateway and a skills registry put the tool layer somewhere an enterprise can see it.
Naive RAG splits documents into fixed-size chunks and takes the top k by cosine similarity. It fails silently. What changes when ingestion builds a graph instead of a list.
A raw CLI agent starts every conversation from zero. Workers carry a role, a git-backed knowledge base, and what they learned last session — with the telemetry a platform team needs.
Cloud IAM decides which principal may touch which resource. It cannot express that a claims specialist may use an application but not authorize a payment. That gap is where AI governance lives.
Your provider can tell you what an endpoint consumed. Your business wants to know what customer support cost, which agents contributed, and how much was error recovery.
You do not need heavyweight governance before your first useful application. You do need to avoid the architectural choices that assume the first agent will be the only one.
Architecture reviews compare licence costs and feature lists. They rarely count the vendor review, the security assessment, the billing relationship, and the skill nobody has yet.
A database is not responsible for deciding who may approve a workflow. Every technology draws a boundary around what it solves. In enterprise AI, few buyers read the boundary before signing.
Data sovereignty, compliance, cost control, and operational independence. The case for self-hosted AI agents over hosted platforms.
RPA handles the happy path. AI agents handle reality. How intelligent workflow automation goes beyond scripts to reason through exceptions.
Structured log aggregation and session-correlated traces for full observability into AI agent workloads in production.
How the Notification Service enables email, SMS, and webhook notifications for AI agents with built-in idempotency and provider abstraction.
Part 3 of 3: Row-level security, scratch pad working memory, named entity recognition with fuzzy matching, and cross-database federation.
Part 2 of 3: Ontology-driven business domain modeling, process rules that encode tribal knowledge, and AST-based queries that eliminate SQL injection by construction.
Part 1 of 3: How DAS gives agents governed access to 12+ database backends, semantic views that reshape data without touching source systems, and a dictionary that turns raw schemas into business knowledge.
PullChain pipelines, hierarchical task pools, and dependency-aware scheduling — the async primitives powering FireFoundry's concurrent agent workflows.
Five failure modes, five pieces of infrastructure. What actually closes the gap, taken one failure at a time.
Five failure modes kill AI projects between the demo and production: brittleness, the black box, state amnesia, security theater, and testing impossibility.
Build a complete AI-powered document processing pipeline with human-in-the-loop review using FireFoundry's SDK.
Introducing FireIQ, an AI-powered conversational BI solution that lets you query databases in plain English.
AI agents are fundamentally different from chatbots. Learn what makes an agent agentic and why this distinction matters for enterprise adoption.
A step-by-step tutorial for building and deploying your first AI agent with FireFoundry in under 30 minutes.
Deep dive into how the Entity Graph provides zero-code persistence for business objects and enables agent memory across sessions.
Build, govern, scale, monitor: the four stages between an AI experiment and a system someone can own.
How AI agents transform customer service from scripted responses to intelligent, context-aware conversations that resolve issues.
When AI fails in production, debugging is notoriously difficult. Learn how full observability transforms incident response.
Announcing Ensemble Groups, a new feature that enables multiple LLMs to collaborate on complex reasoning tasks.
Tutorial: Build an AI agent that extracts information from documents, classifies content, and routes to appropriate workflows.
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