01
Unpredictable Costs
Recursive agent loops burn 3x–5x more tokens than estimated — and you only find out when the invoice arrives. 82% of agent builders say cost predictability is the #1 blocker to scaling.
Welcome to the world of Agents
Hello from Project Nebula
If your models can be trained,
why not your agents
Real-Time AI Agent Interception & Pre-Execution Cost Control. 100% Self-Hosted.
Welcome to the world of Agents
The Industry Gap
The agents are running. Nobody's watching.
02
Zero Policy Enforcement
Same prompt, same context — different execution path every time. Agents ignore company rules, compliance boundaries, and deterministic workflows mid-run because nothing enforces them during the loop.
03
Data Leaves the Building
Every cloud API call transmits your proprietary code, customer data, and internal docs to servers you don't control. Enterprise migration to self-hosted open-weight models grew 4x in 2025–2026 for exactly this reason.
Solution 01 — Mid-Way Trajectory Interception
Catch it mid-flight. Not in the post-mortem.
Nebula monitors every step of an agent's execution loop — tool selections, parameter payloads, chain-of-thought decisions — in real time. When a rule is violated or a trajectory drifts, Nebula corrects the agent's course mid-cycle, before the action becomes irreversible. Post-hoc tools show you what went wrong after the run completed. By then the tokens are burned and the wrong write is committed.
< 5ms P99 interception latency. Your agents don't feel it.
Solution 02 — Pre-Execution Cost Predictor
Know the cost before you pay it.
Before any LLM call is dispatched, Nebula's resource weight matrix inspects the full payload — context length, tool invocation count, model pricing, retry probability — and calculates the exact projected cost of that step. Over threshold? The call is paused, rerouted to a lighter model, or blocked entirely. No tokens consumed. No surprise bills.
3x–5x cost overruns come from uncontrolled recursive loops. Nebula stops them before the first token fires.
Solution 03 — Strict Regulatory Policy Layer
Your rules. Enforced every single run.
You define the operational playbooks — compliance boundaries, action whitelists, formatting mandates, workflow constraints. Nebula enforces them as hard, deterministic execution policies no agent can bypass or creatively reinterpret. This isn't prompt-level guidance; it's a policy engine that blocks non-compliant tool calls before they fire.
EU AI Act Article 14 mandates real-time active intervention. Post-hoc logs don't comply.
Solution 04 — Self-Updating Playbooks
Your agents stopped repeating the same mistakes.
Most frameworks run on static playbooks: a mistake in cycle 3 repeats in cycle 4, 5 and 47. Nebula folds validated outcomes back into the playbook after each execution cycle. The core structure stays fixed and deterministic; the execution strategy evolves on real outcome data — automatically. No prompt hacking. No retraining.
Continuous feedback loop. Zero human intervention. Agents that get better every cycle.
Solution 05 — 100% Self-Hosted
Your infrastructure. Your data. Zero exceptions.
Nebula deploys directly on your servers — bare metal, Docker, Kubernetes, private VPC. There is no cloud component. No telemetry. No "call home." Zero bytes of your data ever leave your network boundary. Every other tool in this space runs on multi-tenant cloud infrastructure — Nebula is built from day one for total air-gapped, on-premise deployment.
Single binary. Docker-native. Deploys on existing Kubernetes infrastructure. Zero additional cloud dependencies.
The Status Quo
What exists today — and why it's not enough.
| Capability | Post-Hoc Loggers | Passive Gateways | Nebula |
|---|---|---|---|
| When It Acts | After execution ends | Before/after API call | Mid-execution — real time |
| Cost Control | Billing dashboards | Rate limiting | Pre-execution cost prediction |
| Self-Learning | Static | None | Self-updating playbooks |
| Data Privacy | Multi-tenant cloud | Multi-tenant cloud | 100% self-hosted |
| Compliance | Audit logs | Basic filtering | Real-time active intervention |
| Examples | LangSmith, Arize, Langfuse | Portkey, LiteLLM | Project Nebula |
Final Boarding Call
Train your agents.
Not your excuses.
Project Nebula is opening early access to teams running autonomous agents in production. Self-hosted, real-time, and built to stop bad runs before they cost you.
Request Early AccessTakes less than a minute · Google Form