Sentie. The Automated AI Consultant

Most "AI agent" products are chatbots with a fancy wrapper. They answer questions. They summarize documents. They draft emails you'll rewrite anyway.

We built Sentie because we got tired of that gap — the distance between what AI demos promise and what actually runs in production.

Here's what we learned building a platform that deploys autonomous AI agents for real businesses.

The Problem Nobody Talks About

AI agents fail at multi-step tasks. Not sometimes — almost always.

The math is brutal: an agent that's 85% reliable per step drops to 20% success over a 10-step workflow. That's not a rounding error. That's a product that works in demos and breaks in
production.

The root cause isn't the LLM. It's the architecture. LLMs predict the next token. They don't predict the next state of your business. There's no internal model of what happens when the
agent clicks that button, sends that email, or updates that record.

We spent a year fixing this with a different approach.

World Models > Token Prediction

Sentie runs on Stratus X1, a JEPA-based world model that learns how a business actually operates — not just what words appear in the data.

The difference in practice:

# What an LLM-only agent does:
"Customer asked about refund" → predict next token → "I'll process that for you"
# (But it doesn't know your refund policy changed last week)

# What a world-model agent does:
"Customer asked about refund" → simulate: check policy (updated 4/15) →
check order status → check return window → predict outcome →
"This order is outside the 30-day window per our updated policy.
Offering store credit instead."

The world model simulates consequences before acting. That's what turns a chatbot into an operator.

What We Actually Deploy

Every Sentie client gets a fleet of custom AI agents configured for their specific workflows:

For an e-commerce brand:

• Order tracking agent (connected to Shopify)
• Customer support agent (handles tier-1 tickets autonomously)
• Inventory alert agent (predicts stockouts before they happen)
• Returns processing agent (with approval gates for edge cases)

For a marketing agency:

• Client reporting agent (pulls data, generates reports weekly)
• Content scheduling agent (manages the publishing calendar)
• Lead qualification agent (scores inbound and routes to the right person)

For a CPA firm:

• Document intake agent (extracts data from uploaded tax docs)
• Client communication agent (sends status updates, requests missing info)
• Deadline tracking agent (monitors filing deadlines across all clients)

Each agent connects to 45+ tools natively — Slack, Gmail, HubSpot, Salesforce, Shopify, Stripe, QuickBooks, and more. No Zapier glue. Direct integrations.

The Part Everyone Skips: Human Oversight

Here's what we think most AI companies get wrong — they either go full autonomous (scary) or full human-in-the-loop (defeats the purpose).

We built a middle layer:

• Approval gates — High-impact decisions route to a human. Low-impact ones execute autonomously. You set the threshold.
• Budget controls — Per-agent spending limits with hard caps and real-time tracking.
• Emergency pause — One button stops everything. Instantly.
• Dedicated Success Manager — Every client gets a human who configures, monitors, and improves the agents. Not a help desk. A person who knows your business.

We charge $299/mo for Starter, $499/mo for Pro. That's less than one part-time hire.

The Technical Stack (For the Devs)

If you're building agents yourself and want to understand the architecture:

Stratus X1 API endpoints:

• /encode — Convert any state description to a 768-dim embedding
• /rank — Score candidate actions by predicted outcome
• /proximity — Measure distance between current state and goal
• /rollout — Simulate a full multi-step trajectory before executing

The integration is a two-line change:

# Before (OpenAI direct)
client = OpenAI(api_key="sk-...")

# After (Stratus + OpenAI)
client = OpenAI(
api_key="stratus_sk_live_...",
base_url="https://api.stratus.run/v1"
)

# Everything else is identical. 75 model combinations available.

75 model combinations: 5 Stratus sizes × 15 LLM options (GPT-4o, Claude Sonnet, Haiku, etc.). Pick your world model size and your reasoning engine.

Full docs at docs.stratus.run.

We Run Sentie on Sentie

The best proof that the technology works: our own GTM, lead qualification, content generation, and customer onboarding are handled by the same agents we deploy for clients.

2,613 agents live. 1,286 businesses analyzed. 99.9% uptime.

If your AI can't run your own business, you probably shouldn't be selling it to run someone else's.

What I'd Tell You If You're Evaluating AI Agents

Skip the feature matrix. Ask three questions:

1. Does it build a model of MY business, or does it just pattern-match? (See how we compare)
2. What happens when it's wrong? (If the answer is "it won't be wrong" — run.)
3. Who's accountable when it breaks at 2 AM? (If there's no human, that person is you.)

Start with a free AI analysis. No pitch deck. No six-week discovery phase. Just a clear picture of where agents fit in your operation.

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Building in public from Miami. Questions? Find us at sentie.io or @sentieai on X.