Every enterprise wants AI agents. The promise is irresistible: intelligent software that can reason, act, and deliver results with minimal human supervision. But there is an uncomfortable truth that anyone who has tried to ship an AI agent into production already knows -- the gap between a working prototype and a production system is enormous.
A demo that impresses stakeholders in a meeting room falls apart the moment it encounters real-world complexity. Models hallucinate. Responses come back in unexpected formats. Provider APIs go down at 2 AM. There is no audit trail, no way to debug what happened, and no mechanism to gracefully degrade when things go wrong. The prototype works; the product does not.
This is not a failure of AI itself. It is a failure of infrastructure. And that is the problem we built FireFoundry to solve.
Today, we are announcing FireFoundry: an Enterprise AI Operating Environment -- the opinionated operational layer for building, owning, governing, and evolving AI systems across their full lifecycle. FireFoundry is available in private beta.
What is an Enterprise AI Operating Environment?
It is the layer that turns models, frameworks, clouds, and specialist services into governed enterprise capabilities. It provides a consistent way to build and operate AI applications while organizing the recurring decisions around the concepts the business actually owns: applications, agents, knowledge bases, departments, users, policies, cost centres, approvals, and lifecycle stages.
Here is the part that surprises people. You already have one. If your organization has combined a framework, a cloud platform, a model gateway, an observability service, some security controls, internal administration, and a set of project-specific conventions, that combination is your operating environment. You have not avoided the category. You have built your own version of it, usually without naming it, staffing it, or deciding who owns it.
The term is deliberate. It draws a parallel to an operating system without claiming to be one. An operating system does not replace processors, storage, networks, or applications -- it coordinates them, provides shared abstractions, establishes lifecycle rules, and gives many users and programs a consistent way to consume the resources underneath. An Enterprise AI Operating Environment plays an analogous role for AI capabilities spread across clouds, models, frameworks, data systems, and specialist services.
Which makes the real decision a familiar one: build or buy. A build path can use excellent commercial and open-source components, but your enterprise remains the system integrator and the owner of the gaps between products -- deciding how the parts fit, establishing governance across them, building the missing administration, reconciling telemetry, managing upgrades and contracts, and working out where support responsibility changes hands. A buy path transfers responsibility for the integrated environment to a product and a delivery partner.
FireFoundry is the buy path. Your team focuses on business capability; we own the environment around it.
Build, Govern, Scale, Monitor
FireFoundry organizes the lifecycle into four stages, served by one control plane. These are not aspirational goals -- they are the stages at which an AI capability either becomes something the enterprise can own, or does not.
Build
You still have to build the agent, and an environment that begins only after development arrives too late. Model your domain as typed entities, put the reasoning in bots, and coordinate them with workflows -- each piece testable on its own before anything ships. The Agent SDK is the developer's front door and the point where the platform's opinions become useful rather than abstract, with deterministic local testing through model mocking so "it worked in staging" stops being a sentence anyone says.
Govern
Governance is usually treated as a review step after a prototype succeeds. By then the architectural decisions are embedded in code, credentials are distributed, and ownership is ambiguous. In FireFoundry, access control, policy and PII redaction, and human approval sit on the request path itself, and every step is written to the audit log.
We mean something specific by governance. It is the ability to identify enterprise AI subjects, objects, actions, and context; assign ownership; express policy over their relationships; enforce that policy during operation; and preserve an auditable lifecycle. Cloud IAM can determine which technical principal may access a resource. It cannot, on its own, express that a claims specialist may use a particular application but may not authorize a payment, or that a refund above a threshold requires a manager, or that a knowledge steward must approve a source before production use. Those are governance decisions, because they connect identity, business meaning, permitted action, context, and accountability.
Scale
One team can make local decisions for one agent. Ten teams operating dozens of agents cannot independently choose governance models, telemetry conventions, routing policies, and credential practices without producing fragmentation. At portfolio scale, repeated local freedom becomes enterprise complexity.
The Broker fronts every model behind one gateway: group deployments across providers, choose a selection strategy, fail over automatically, and promote the same bundle from development to production. Approve a provider once and let many agents inherit the policy. This is decision leverage -- the platform reduces not just the difficulty of individual tasks but the number of decisions, tools, and operating models your organization has to keep in its collective memory.
Monitor
Infrastructure monitoring reports resource health. Agent observability traces model calls. Neither tells the enterprise which business capability cost what, or who is accountable when it breaks. FireFoundry connects a request to the agents, model calls, searches, tools, retries, and costs that fulfilled it -- then attributes the result to an owner. Open any run in the Console and inspect it node by node: input, output, logs, progress, working memory, with traces and alerts across every run.
What Sits Inside the Boundary
Every platform draws a boundary around the problems it chooses to solve. The useful question is not whether a problem can be solved with existing services -- it usually can. It is whether every enterprise should have to solve it independently. Here is what sits inside ours:
- TypeScript Agent SDK -- Build agents using a first-class SDK with built-in support for the Entity Graph (persistent business objects), Bots (conversational agents), and Workflows (multi-step orchestration). Define your domain model and the SDK handles persistence, state management, and lifecycle.
- ff-cli -- A command-line tool for scaffolding new agent projects, managing deployments, running local development environments, and administering your FireFoundry installation. Get from zero to deployed agent in minutes.
- Runtime Services -- The microservices that power your agents: the Broker for model routing, Entity Service for persistent state, Context Service for conversation memory, Knowledge Service for managed knowledge bases, Data Access for governed database access across a dozen backends, Document Processing for PDF and Office handling, Web Search, Sandbox for secure code execution, Telemetry and Log Proxy for observability, and Notification for delivery.
- Tools and Extension -- An MCP Gateway that consolidates Model Context Protocol tools and resources in one governed place, a Skills Service for uploadable versioned capability packages, and Virtual Workers for managed session-based AI code generation.
- Identity † -- A canonical model of principals, groups, and permissions that is independent of your identity provider, so policy can name the people who use an application rather than only the administrators who deploy it.
- Management Console -- A web-based operations dashboard for monitoring agent performance, searching logs, managing environments, configuring RBAC, and visualizing your agent fleet in real time.
- Functions-as-a-Service † -- Where an agent's useful work goes to live. The sandbox runs ad-hoc code; when something proves worth keeping, it can be promoted to a named function that agents, applications, or anything else can call over HTTP -- no environment to manage in order to reuse it. Promoted functions run in FireFoundry-provided runtimes that carry the caller's identity through to downstream services such as governed data access, and their routes carry access-control groups. That identity path is still being hardened, which is what the dagger marks.
- Cloud Manager † -- Discover existing cloud resources and keep environments consistent across development, staging, and production clusters.
- Marketplace -- A catalog of pre-built applications and agent templates. Launch proven solutions like FireIQ (intelligent Q&A) or AI Training Portal rather than building everything from scratch.
Built for AI-First Development
There is an important philosophical distinction that sets FireFoundry apart from other platforms in the agent space. We are not building a low-code tool. We are not building a visual workflow editor. We are building infrastructure for real code written by real developers -- and increasingly, by AI coding assistants working alongside them.
The FireFoundry SDK is designed to be AI-friendly. Its APIs are clean, well-documented, and follow consistent patterns that AI coding tools understand naturally. You can "vibe code" a simple agent -- describe what you want to a tool like Claude Code or Cursor, and it can generate a working FireFoundry agent from the description. For complex agents with sophisticated business logic, you architect the system yourself while letting AI assistants handle the scaffolding and boilerplate.
This is the development workflow we believe will define the next era of software engineering: humans set the direction and make architectural decisions, AI assistants accelerate the implementation, and the platform handles the infrastructure. FireFoundry is purpose-built for this model.
Where declaration genuinely fits, you do not need TypeScript at all. FireFoundry's declarative bundle runtime † lets you define entities, workflows, bots, and prompts in markup and have the runtime bootstrap them. Note what that is not: it is not a visual builder. We removed the code without adding drag-and-drop.
We go further still. FireFoundry provides its own Virtual Workers -- AI-powered coding assistants that understand the FireFoundry platform deeply. These are not generic code completion tools. They understand your Entity Graph schema, your Bot configurations, your deployment topology. They can help you build, debug, and optimize agents with platform-specific knowledge that general-purpose assistants lack.
Now in Private Beta
FireFoundry is available today in private beta. We are working closely with a select group of design partners to refine the platform before general availability. Our beta partners span industries -- from financial services firms building compliance agents to healthcare organizations deploying clinical decision support, to technology companies creating AI-powered developer tools.
If you are building production AI agents and tired of reinventing infrastructure, we want to hear from you. Request beta access by contacting us at firefoundry@firebrand.ai. We are particularly interested in teams that have experienced the prototype-to-production gap firsthand and are looking for a better way.
Looking ahead, we are expanding the FireFoundry Marketplace with new pre-built applications. FireIQ, our intelligent Q&A platform, is already available for beta partners. The AI Training Portal, which enables organizations to build and deploy custom training programs powered by AI agents, is coming soon. We envision the Marketplace becoming a thriving ecosystem where organizations can share, discover, and deploy agent solutions.
The Road Ahead
Every important technology transition eventually develops an operational layer. The web evolved beyond pages into content management and application platforms. Virtualization developed infrastructure management. Containers developed orchestration and platform engineering. Enterprise AI is moving through the same transition, on a compressed schedule.
The industry has made extraordinary progress at building intelligent agents. Production success now depends on the systems around them: ownership, governance, lifecycle, administration, operational intelligence, and the ability to evolve without rebuilding the foundation for every new application. The centre of gravity is shifting from technical possibility to operational ownership.
We are not claiming that every component of FireFoundry is unprecedented, or that cloud platforms and developer frameworks are inadequate. They are advancing quickly and they are good at what they set out to do. The durable distinction is architectural: each platform establishes a responsibility boundary, and an operating environment is defined by the recurring enterprise responsibilities it intentionally brings inside that boundary.
The organizations that succeed at this will not merely build better agents. They will become better at owning them. The environment is inevitable; the integration burden is a choice.
† In beta or active development. We mark these rather than blur them -- a roadmap is only useful if you can tell it apart from what ships today.
Ready to get started? Visit the developer documentation to explore the SDK and platform architecture, or request beta access to start building today.