Enterprise AI is failing at
the infrastructure layer, not the model layer.
Swap GPT-4 for Claude. Swap Claude for Gemini. The cascade persists. The problem was never the model — it was the space between enterprise systems and models that nobody built.
The failure pattern
Every enterprise AI deployment hits the same wall. Not at the model. At the infrastructure:
Repeating across every deployment
No shared memory across agents
↓LLM reasons on incomplete context
↓Human patches the gap manually
↓Tool layer receives inconsistent inputs
↓Next agent starts from corrupted state
↓Repeat
This is not a hallucination problem. Hallucination is a symptom. The cause is architectural: agents have no shared memory, no persistent state, and no mechanism to verify that what they received is coherent before they act on it.
The research is unambiguous. A 2024 paper (arXiv 2401.11817) provides the mathematical proof — LLMs cannot learn all computable functions and will inevitably confabulate as general problem solvers. The fix lives at the input and output boundaries. Not inside the model. Meanwhile, 63.6% of enterprise RAG deployments are still running standard vector retrieval — the baseline approach that doesn't address retrieval coherence at all.
Swap the model. The cascade continues.
Where RAG breaks
Standard retrieval finds what looks like the answer. Enterprise reasoning requires finding what caused the problem. These are not the same operation — and confusing them is why multi-agent systems fail in production even when every individual component works correctly.
The scenario every infrastructure engineer eventually hits:
A team migrates from PostgreSQL to MongoDB. Six months later, user search returns inconsistent results. The agent queries for the root cause. It retrieves recent bug reports, the search codebase, deployment logs — everything semantically related to the symptom. It never retrieves the migration document, titled “Q3 Infrastructure Modernization Plan,” because “user search inconsistency” and “Q3 infrastructure modernization” share zero vocabulary. The migration is the root cause. Standard retrieval cannot traverse the causal chain to find it.
This isn't an edge case. It's the default failure mode of any system operating on enterprise knowledge — because enterprise knowledge is fundamentally causal: decisions cause states, states cause incidents, incidents cause patches, patches cause new states. Similarity search was never built to traverse that graph.
Reduction in ticket resolution time when structured causal retrieval replaced standard RAG in production. The context was identical. The retrieval architecture was different.
The ceiling isn't the model. It's the retrieval layer beneath it.
The problem that compounds it
Retrieval failure is the first break. The second is less visible and more dangerous.
When agents delegate to agents, the original human intent degrades at every handoff. Each agent reasons correctly on what it received. The original goal simply doesn't survive translation. By the time the fourth agent in a chain acts, it may be optimizing for something meaningfully different from what the human intended.
This is not solvable at the model level. Current agent frameworks have no mechanism to detect or prevent the drift. The result: multi-agent systems that work in demos and fail in production — not because of model capability, but because intent was never preserved past the first context window boundary.
10ˣ is the missing infrastructure layer between enterprise systems and LLMs.
Not a workflow tool. Not an AI product. The architectural substrate that makes enterprise AI reliable — by solving context loss at every layer where it occurs.
It connects to the systems your organization already runs — Jira, Slack, GitHub, PagerDuty, Confluence. At ingestion, it builds causal memory, not a flat index. Every event is written with its relationships intact: what caused it, what it caused, what decision preceded it, what it changed. At query time, it routes by intent and traverses causal chains — not just similarity clusters. The model receives coherent, causally-ordered context instead of a bag of similar-looking chunks.
The system compounds. Every action writes back. Every deployment adds causal density. After months of operation, 10ˣ holds an irreplaceable institutional record — every decision, delegation, and outcome across every enterprise system, linked causally and queryable by any agent in the chain. That record deepens every day.
The natural objection
Larger context windows will eventually make this irrelevant.
They won't. Information in the middle of long-context windows degrades 20–40% on retrieval tasks — not a bug in a specific model, but a structural consequence of how attention works at scale. At very long sequences, softmax attention weights become nearly uniform across positions. The model averages context rather than reasoning from it.
A 1M-token context window filled with incoherent, stale, fragmented enterprise data produces worse output than a 4K-token window with precisely relevant, causally-ordered context.
Model providers are optimizing the transformer. 10ˣ optimizes what the transformer receives. These are different problems. The second is the one enterprise AI actually fails on — and it remains unsolved regardless of context window size, model scale, or inference speed.
Every improvement in frontier models makes the integration layer more valuable, not less.
Early access
10ˣ is in active development. We are not taking on general customers.
We are talking to three kinds of people:
Operators who have deployed multi-agent systems and hit the retrieval ceiling or the goal drift problem in production. You know exactly which failure mode we're describing because you've spent engineering cycles building around it.
Investors who understand that infrastructure compounds and that the write layer — what gets stored and how — is the moat that retrieval-only approaches cannot replicate. The value does not come from a better model. It comes from a causally-linked institutional memory that grows denser with every deployment.
Design partners building on multi-agent orchestration stacks who need a shared memory layer they don't have to build themselves — model-agnostic, causally-aware, production-hardened from day one.
If you've shipped agents into enterprise production and want the infrastructure layer that makes them reliable: we want to talk.
Early conversations only. No decks, no demos — just context on what you're building and where you're hitting the wall.