Agents forget everything between sessions.
Every interaction starts cold. Context resets. Decisions made yesterday are gone tomorrow. Memory is the first layer that breaks.
Nexus-PRIME Labs · AI infrastructure
The operating stack
for enterprise AI agents.
Five layers. One integrated system. Built for teams running agents in production — not slideware.
5 layers · 1 stack · live
01 · Why this exists
Shipping the model takes a week. Shipping the stack around it takes six months.
Every team that puts AI agents into production lands in the same room. The model works. The demo works. Then five things break — every time.
five failure modes · five layers in the stack · one for each
The context window is the bottleneck.
Long-running workflows blow the window. Tools fight for tokens. Compression is mandatory — not optional — once your agent does real work.
Agents don't speak production.
Without a protocol layer, every integration is bespoke glue. Every handoff leaks state. Every audit becomes archaeology.
Naive loops are expensive and slow.
Cold paths stall. Replays cost double. The runtime decides whether your agents make money or burn it on every task.
Long-horizon work needs missions.
Without a mission layer, agents drift. Tasks lose intent. Multi-day work becomes unreviewable. You can't operationalize what you can't direct.
02 · The stack
Five layers. One operating system.
Use any layer independently. Compose them when you need the full system.
01
Layer 01 · control plane
Nexus Prime
agent memory at its core
The control plane every multi-agent system eventually needs. Persistent memory, token-aware planning, repo-aware file selection, and review gates — wired into one orchestrator that Codex, Claude, Cursor, and your own agents can call.
- Persistent memory across sessions
- token-aware planning
- review gates
- multi-client MCP surface
OPEN Inspect →
02
Layer 02 · compression
tetris.codes
fits the window
Real compression for real agents. Tetris collapses prose, code, and traces with a multi-channel pipeline so long coding-agent sessions don't blow the context window — and don't blow the bill.
- Multi-channel compression
- token-aware reads
- MCP-native
- drop-in for Claude, Codex, Cursor
OPEN Inspect →
03
Layer 03 · protocol
Grain
carries it cleanly
Production agents need to read the business — its processes, its artifacts, its rules — without bespoke glue for every integration. Grain is the protocol layer that makes workflows agent-readable so handoffs stop leaking.
- Agent-readable workflow schemas
- typed handoffs
- audit-grade artifact trails
ENTERPRISE Talk to us →
04
Layer 04 · runtime
NXL
runs it cheap
Naive agent loops burn tokens and stall on cold paths. NXL is the runtime — speculative execution, cache-warm planning, deterministic replay — that turns agent loops into a unit-economical execution layer.
- Speculative execution
- cache-warm planning
- deterministic replay
- token unit-economics
ENTERPRISE Talk to us →
05
Layer 05 · missions
Phantom
directs the work
Real agent work spans hours, days, weeks. Without a mission layer agents drift, work loses intent, and review becomes impossible. Phantom shapes long-horizon work into mission packets your organization can route, remember, and review.
- Mission packets
- route + remember + review
- long-horizon traceability
ENTERPRISE Talk to us →
One prompt. Five layers. Every tool call traceable.
Illustrative trace from the control plane. Tool names are real. Values are sample data.
Nexus Prime · Cowork
live
opus-4-7 · routed
Onboard a new customer support workflow. Recall last week's audit, compress the playbook into our context budget, route through the protocol layer, and ship the mission packet.
nexus_recall_memory 7 results · 2 high-priority
tetris_compress_text playbook · 11.4k → 3.6k tokens
grain.protocol.bind workflow.support.v2 · typed
nxl.runtime.dispatch specialist · pool sized 3
phantom.mission.commit packet · sealed · routable
Mission packet support.onboarding.v2 is sealed. Memory recalled, context compressed, protocol typed, runtime pooled. Hand it to your operator — every step is replayable.
Sample trace — tool names match what the stack actually exposes. Values are illustrative; no fake benchmarks are claimed.
03 · Engage
Three ways to ship AI infrastructure with us.
Install the open layers. Pilot a custom integration. Or have us build the stack into your product.
Install
Use the stack directly.
Drop our shipped layers into your existing agents. Install commands, docs, and product sites are live today.
See install commandsPilot
Co-build one workflow.
Bring one painful agent workflow. We pair the right layers and ship a working integration in a tight loop.
Talk to usBuild
We build it for you.
Custom AI infrastructure built on the stack. End-to-end engagement for enterprise teams that need it shipped.
Start a project 04 · Proof
What you can install today.
Two layers public. Three running in pilot integrations. Everything below is real, installable, and inspectable.
control plane
LIVE
Nexus Prime
The control plane every multi-agent system eventually needs. Persistent memory, token-aware planning, repo-aware file selection, and review gates — wired into one orchestrator that Codex, Claude, Cursor, and your own agents can call.
$ npm i -g nexus-prime compression
LIVE
tetris.codes
Real compression for real agents. Tetris collapses prose, code, and traces with a multi-channel pipeline so long coding-agent sessions don't blow the context window — and don't blow the bill.
$ curl -fsSL https://get.tetris.codes | sh 05 · Fast track
Skip the form chains. Email us in 30 seconds — from your inbox.
We don't capture your address. You stay in your own mail client (Gmail, Outlook, or whatever your company uses) and send the message you want, with the subject and body already prepared.
Tell us about the workflow.
Three fields, one click. We open your mail client with a tailored message — you review, then send.
06 · How we work We ship the stack.
We ship the stack.
You ship the product.
We are an AI Labs, not a model company.
Models commoditize. The operating stack around them is the durable advantage.
Five layers because the problem is five-shaped.
Memory, compression, protocol, runtime, missions. Each layer is independently useful. Together they form an operating system for agent work.
Built for enterprise. Sold by founder.
We work directly with the teams shipping the agents. No reseller chains, no theater dashboards, no invented metrics.
The word is stack because the pieces depend on each other.
You can take one layer. Most teams need three. The full stack is the differentiator.