The Story
Your AI Agents Aren't in Production.
Here's Why.
It's not the models. It's not your team. It's the infrastructure underneath — and the fact that it was never designed for what you're asking it to do.
You know how this goes. You've lived it.
Your team picked a framework. They stood up a vector database. They found a monitoring tool, started building a governance layer because nothing on the market actually covered it, and wired everything together with integration code.
Eighteen months later, you have a demo that impresses the board and a production deployment that your security team won't sign off on.
Only 11% of enterprises have AI agents running in production today. Gartner says over 40% of agentic AI projects will be canceled by 2027 — not because the AI isn't smart enough, but because the infrastructure wasn't built for it.
Your engineers spend their time on plumbing.
Seventy percent of their week goes to wiring things together — connecting the framework to the vector database to the monitoring tool to the workflow engine to the governance layer. Thirty percent goes to the actual business logic. Every connection between those systems is a place where bugs live, context gets lost, and costs go untracked.
Security keeps saying no.
And they're right to. They ask straightforward questions: Can an agent call an API it shouldn't? Can it blow through its budget? Can you stop it mid-task if something goes wrong? Honest answer: not reliably. Because governance was added after the system was designed. It's a layer on top, not the foundation. So deployments stall.
Only your AI team can use what you've built.
Maybe ten people understand the pipeline well enough to work with it. The compliance team can't touch it. Operations can't inspect it. Legal can't audit it. Everything requires code, or config files, or knowing how the retrieval pipeline works. Your AI investment serves ten people instead of the ten thousand who could benefit from it.
You didn't build a system. You assembled one.
And the seams between the pieces — between your framework and your vector database and your monitoring tool and your governance layer — are where everything breaks. Governance gaps at the boundaries. Context lost in translation. An audit trail with holes you only discover during an incident.
Better integrations won't fix this. Tighter APIs between six vendors don't eliminate the seams — they just make them more reliable. What changes everything is infrastructure where execution, governance, observability, memory, knowledge, and integration were designed together from the start.
That's what we built. That's Forge.
Three things that change.
Same information your team needs to hear. Different answers than you're used to giving.
For your security team
The bypass doesn't exist.
Your CISO asks: "Can an agent call an unauthorized model or API?"
Today, the honest answer is probably "we try to prevent it." There's a policy layer, a proxy, a monitoring tool that flags violations after they happen. But the agent code can still make raw API calls. The governance layer catches most of them. Most.
In Forge, the answer is different: the capability doesn't exist. There's no raw API call available in agent code. The only way an agent can reach an AI model is through a governed path that checks your approved model list, enforces budget limits before the call executes, and logs everything. If the URL isn't on the approved list, the request never leaves the sandbox. If a task needs human review, execution pauses and waits.
Not intercepted after the fact. Not flagged in a dashboard. Structurally impossible.
Show your CISO this section. It changes the conversation from "we try to prevent it" to "the architecture doesn't permit it."
For your knowledge team
Your documents become usable without leaving your control.
Today, getting a PDF into your AI pipeline is a project. Extract the text. Handle the scanned pages. Chunk it. Embed it. Push it to the vector database. Hope the formatting survived. Debug when it didn't.
In Forge, you upload the file. Any format — PDF, Word, HTML, scanned images. The platform handles detection, asks before it acts, and processes everything: OCR for scanned pages, conversion for formats, chunking, embedding, ingestion. The document is searchable from chat within minutes. Available to agents. Available to investigations.
The entire pipeline stays inside your governed environment. No external services. No data leaving your boundary. Your compliance team doesn't need to review a new vendor — because there isn't one.
For your budget
AI costs stay predictable without manual babysitting.
You've seen the runaway cost problem. An agent makes more model calls than expected. You find out when the invoice arrives. The dashboard shows what happened, but the money is already spent.
In Forge, budgets are enforced before execution, not reported after. Every model call checks whether the projected cost would exceed the cap. If it would, the call is rejected before it's made. Tools load when needed and unload when finished — they don't sit in the context window burning tokens. The AI discovers capabilities on demand instead of carrying everything at once.
Your costs are governed by the same system that runs your agents. Not a separate dashboard. Not a monthly reconciliation. The same runtime, the same budget check, every single call.
What changes in practice.
Time your engineers spend on infrastructure instead of business logic
Time from idea to deployed agent
Your security team's answer to production deployment
How much longer do you want your team building plumbing?
Join the waitlist for early access.
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