The Problem with Current Agents
LLM-based agents achieve only 10–20% success rates on real-world benchmarks like WebArena. Human performance on the same tasks: 78%. The gap is structural, not a prompting problem.
No State Understanding
Agents can’t represent where they are in a task. Every step starts from scratch with raw, noisy context.
No Consequence Prediction
Without a world model, agents can’t foresee what an action will do before committing to it.
No Transition Reasoning
Multi-step planning is impossible when there’s no mechanism for reasoning over state sequences.
Token-Level Reasoning
Processing raw tokens at 15,000+ per task is slow, expensive, and fundamentally noisy.
The Stratus Solution
Stratus doesn’t replace your LLM. It makes your LLM dramatically more effective by handling the parts LLMs are fundamentally bad at — state representation, consequence modeling, and action sequencing.Compress the Environment
Stratus encodes any observation — a webpage, a UI state, a tool response — into a rich semantic representation. The noise disappears. The meaning stays.
Simulate Before Acting
The world model predicts what the environment looks like after each candidate action, in representation space, before anything executes. Your agent sees the future before committing.
Three Components, One Coherent System
State Encoder
Compresses any environment description into a rich representation. The richer your state, the sharper the plan.
World Model
Simulates what the environment looks like after each action — entirely in representation space, before anything executes.
Planning Layer
Sequences actions toward the goal using the world model’s predictions. Returns a ranked plan with confidence at each step.
Without Stratus vs. With Stratus
Without Stratus
Raw observations flood the LLM with 15,000+ tokens of noisy context. The model guesses at each step, with no ability to foresee consequences or recover from mistakes. Success rates hover at 10–20%. Every failure is expensive.
With Stratus
Stratus extracts meaning from the environment, simulates candidate actions, and hands the LLM a structured plan. Token count drops by over 60%. Success rates double. Failures are predictable and recoverable.
Why This Is Different
Stratus operates in representation space — not token space. This is a fundamental architectural distinction from every other approach:vs. RAG
RAG retrieves documents. Stratus learns state transitions. Retrieval doesn’t tell you what happens next — a world model does.
vs. Prompting
Better prompts reorder text. Stratus predicts outcomes in embedding space. No prompt can teach an LLM to simulate consequences.
vs. Fine-tuning
Fine-tuning adjusts token distributions. Stratus models state — what exists, what changes, what’s next. These are categorically different problems.
Performance
Token Reduction
Over 20x reduction in tokens consumed per task. What took 15,000 tokens now takes under 750.
Hallucination Detection
Better than 75% detection rate on hallucinated actions and fabricated state — caught before they execute.
Prediction Latency
Under 10ms per prediction. Stratus adds no meaningful latency to your agent loop.
Throughput
1,000+ predictions per second — scales with your workload, not against it.
Where Stratus Excels
Web Navigation
Booking flows, form completion, data extraction — tasks where multi-step state tracking is everything.
Multi-hop Reasoning
Research tasks that require chaining searches, synthesizing results, and maintaining goal context across many steps.
Task Automation
Workflow automation, software testing, data entry — high-volume tasks where reliability directly translates to cost.
Structured Environments
Any environment with predictable state transitions — APIs, UIs, robotic action spaces — where consequence modeling compounds.
Model Tiers
| Model | Best For | Prediction Latency |
|---|---|---|
small | Highest throughput, latency-sensitive loops | Under 10ms |
base | Balanced performance — recommended starting point | Under 25ms |
large | Extended context tasks | Under 50ms |
xl | Maximum context and precision | Under 100ms |
What Comes Next
Phase 1 — Now
Text-based meaning model for software agents. Production-ready, OpenAI API-compatible, integrates with any agent framework in minutes.
Phase 2 — Year 2
Multimodal world model: text, vision, and telemetry. Extends Stratus into robotics and physical systems.
Ready to see it in action? The Quickstart gets you running in under five minutes. Or go deeper with the API Reference.