The AI model for agentic workflows
Your AI needs an if-statement.
Sage answers closed-ended questions about any content in ~200ms, with a calibrated confidence score your code can route on.
Real responses recorded from the Sage API. Every scenario is a preset you can run live in the console.
The problem
LLMs are verbose and overconfident. Your code needs an answer, a confidence, and a next step.
Machines need decisions, not paragraphs
Chat models return prose to parse and second-guess. Automation needs a typed answer it can branch on.
Overconfidence is the failure mode
LLMs sound just as sure when they're wrong. Without a calibrated signal, you trust everything or nothing.
The hot path has a latency budget
A judgment call inside an agent loop or a checkout can't wait two seconds for a frontier model to stream an essay.
The missing primitive
LLM intelligence at classifier speed, with a confidence score.
Sage lets machines choose, act, or escalate to a human in 60–200ms, always with calibrated confidence. The missing primitive for agentic workflows: fast enough for the hot path, smart enough to read a system prompt, honest enough to say “I don’t know.”
How it works
Content → Decision + Confidence
Sage is a decision model we built and train ourselves: it answers closed-ended questions instead of generating text. Send content and a question, get a typed answer with calibrated confidence in one round-trip.
- 1
Send content + a question
Any text or list, plus a closed-ended question. Your policy goes in the instructions; Sage reads all of it.
- 2
Get a typed decision
Not prose: an answer field your code can switch on, plus a calibrated 0–1 confidence score.
- 3
Route on confidence
High confidence: act. Low confidence: escalate to a human. That one branch makes automation safe to ship.
# pip install levanto
from levanto import LevantoClient, YesNo
client = LevantoClient(api_key="lv_live_...")
env = client.decide(document, YesNo("Needs compliance review?"))
if env["result"]["confidence"] < 0.8:
escalate_to_human(document) # not sure? a human decides
elif env["result"]["answer"] == "yes":
send_to_compliance(document)Agent-native install
Don’t integrate it. Let your agent do it.
Paste one prompt into Cursor or Claude Code. Your agent installs Sage, then reads your repo and proposes where a decision model would replace brittle rules or a slow LLM call.
- 1
Paste the prompt
- 2
The agent installs the skill
- 3
It finds where Sage fits
levanto-sage-decide.SKILL.md. Drop it in .cursor/skills/ or .claude/skills/
You now have access to Levanto Sage, a decision-model HTTP API that turns content + a question into a structured, machine-actionable decision with a calibrated confidence score (Yes/No, Choice, Scale, Sort, Tags).
1. Install the skill so you know the full API contract:
mkdir -p .cursor/skills && curl -fsSL "https://platform.levanto.ai/api/intelligence/skill" -o .cursor/skills/levanto-sage-decide.SKILL.md
(If you use Claude, put it in .claude/skills/ instead.)
2. Set my Levanto API key as the SAGE_API_KEY environment variable. If I don't have one yet, pause and tell me to mint one at https://platform.levanto.ai (signup includes $1 of free credit, ~1000 decisions).
3. The endpoint is https://sage.levanto.ai. POST /decide with the header `Authorization: Bearer $SAGE_API_KEY`.
4. As a first call, make this decision:
{
"content": "Marketing email draft promotes \"guaranteed 40% returns\" and describes the product as \"risk-free\" for accredited investors.",
"question": { "id": "needs_review", "kind": "yesno", "instructions": "Does this copy require compliance review before send?" }
}
Read the skill for the response shape. Then explore this repository: find places where we already make judgment calls with brittle rules, heuristics, or a slow LLM call (moderation, triage, routing, risk, tool gating, ranking, etc.). Propose 2–3 concrete insertion points, pick the strongest one with me, wire it through /decide, and branch on result.confidence: act when it clears a threshold, escalate to a human when it doesn't.Every closed-ended question your product asks
One API, five answer shapes. Each returns calibrated confidence.
The hot path
We asked three models the same question.
“May support auto-fulfill an export request today by email, without Owner approval or a DSAR?”, replayed at the speed it actually happened. Logs
Recorded run, 2026-07-23.
Run it yourself · $1 free creditUse cases
Where teams point Sage
Judgment calls that usually run on brittle rules, slow LLM round-trips, or human review.
Agent workflows
Gate tool calls, route between models, and escalate when confidence is low: one fast call inside the agent loop.
Ops & support triage
Route tickets, score urgency, and auto-approve the clear cases; everything else escalates to a human.
Moderation & content screening
Tag spam, scams, and policy violations at feed speed, with your community's actual rules as the policy.
Risk & fraud
Judge transactions and account activity inline, so clear cases flow and only the suspicious ones stop.
Data pipelines
Tag, score, sort, and screen records in batch: LLM judgment with a confidence column, no classifier training.
Levanto Labs
We make AI safe for humans.
“Levanto” is Spanish for “I rise”: the warm light of a new dawn where humans, agents and nature prosper together.
We are hiring.