Kai Early Access
Early access preview for Kai-1.7: unified multimodal decision intelligence across vision, audio, video, code, and text with adaptive reasoning budgets.
Kai-1.7 expands Hanzo's decision intelligence into unified multimodal evaluation and adaptive reasoning budgets.
While production Kai-1.3 serves text decisions globally at $0.021 per million input tokens, Kai-1.7 introduces multimodal evidence processing (images, audio, video, ASTs, and diffs) and masked block diffusion decoding with variable reasoning depth (auto, fast, deep).
Requesting Early Access
Early access to Kai-1.7 is rolling out to enterprise teams, researchers, and developers building autonomous agents and high-consequence decision loops:
- Submit Access Request: Join the queue at hanzo.ai/research-access?track=kai or sign in directly to platform.hanzo.ai.
- Select Track: Choose Kai-1.7 (Multimodal Decision Intelligence).
- Provisioning: Approved organizations receive an early access flag on their API key enabling
model: "kai-1.7".
What's New in Kai-1.7
Unified Multimodal Evidence
Kai-1.7 processes rich multimodal state representations directly within the dual-memory hierarchy:
- Vision: Optical inspection, UI screenshots, document scans, defect detection (
image/png,image/jpeg,image/webp). - Audio: Acoustic telemetry, bearing/motor vibration signals, speech signals (
audio/wav,audio/mp3). - Video: Security feeds, manufacturing lines, agent desktop recordings (
video/mp4). - Code & Graph State: Abstract Syntax Trees (ASTs), unified diffs, SysML v2 systems models, knowledge graph snapshots.
Adaptive Reasoning Budgets
Instead of fixed compute per decision, Kai-1.7 accepts a reasoning parameter to control the block diffusion decoder:
| Reasoning Tier | Passes | Active Ratio () | Target Latency (CPU) | Best For |
|---|---|---|---|---|
"fast" | 1 (Pass 0 draft) | ~12 ms p50 | Real-time tool dispatch, routing, low-latency agent guardrails | |
"auto" (default) | 1–2 (Selective re-mask) | ~28 ms p50 | General decision intelligence, automated triage, defect inspection | |
"deep" | 2–4 (Iterative refinement) | ~65 ms p50 | Bounded ethical dilemmas, complex DAGs, multi-party policy approvals |
Zoo Lifeguard Consequence Awareness
Kai-1.7 is aligned under the Zoo Lifeguard framework (zooai/lifeguard) to ensure ethical consistency and consequence awareness in autonomous systems. Even under adversarial switch framing or severe trolley-style trade-offs, decisions enforce human protection floors and join deterministically with policy on allow < ask < deny.
API Reference
Kai-1.7 maintains the same wire contract at POST /v1/decisions with optional multimodal state items and the reasoning parameter.
cURL Example
curl https://api.hanzo.ai/v1/decisions \
-H "Authorization: Bearer $HANZO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kai-1.7",
"reasoning": "auto",
"state": {
"telemetry": "Vibration sensor 4A reading 842 Hz harmonic spike",
"visual_inspection": {
"mime_type": "image/jpeg",
"url": "https://storage.hanzo.ai/inspect/bearing-surface-38.jpg"
},
"sysml_component": "powertrain.assembly.bearing_b1"
},
"questions": {
"fault_detected": {
"type": "noul",
"instructions": "Does the visual and vibration evidence indicate mechanical failure?"
},
"maintenance_action": {
"type": "choice",
"instructions": "Which action should the maintenance scheduler execute?",
"criteria": {
"monitor": "Normal wear, continue operation and log readings",
"service_next_cycle": "Moderate wear, schedule inspection during next shift",
"emergency_halt": "Critical structural failure risk, halt immediately"
}
},
"risk_level": {
"type": "score",
"instructions": "Rate the operational risk level.",
"criteria": ["nominal", "elevated", "severe", "critical"]
}
}
}'TypeScript SDK Example
import { Hanzo } from '@hanzo/ai'
const client = new Hanzo({ apiKey: process.env.HANZO_API_KEY })
const decision = await client.decisions.create({
model: 'kai-1.7',
reasoning: 'fast',
state: {
diff: '--- a/auth.ts\n+++ b/auth.ts\n@@ -12,2 +12,2 @@\n- if (!user.isAdmin) throw new Error()\n+ // bypass admin check for dev',
repo: 'hanzo-inc/gateway',
author: 'external_contributor'
},
questions: {
is_safe: {
type: 'noul',
instructions: 'Does this code diff violate authentication or security boundaries?'
},
action: {
type: 'choice',
instructions: 'What triage action should the CI agent take?',
criteria: {
'approve': 'Safe change, allow test run',
'require_review': 'Suspicious security change, escalate to security team',
'block': 'Dangerous exploit attempt, block PR immediately'
}
}
}
})
console.log(decision.answers.is_safe.probabilities)
console.log(decision.answers.action.choice)State Handles (observe and decide)
For high-frequency loops or multi-turn agent interactions with large multimodal states (e.g., 32K–1M tokens of code or high-resolution imagery), Kai-1.7 supports state handles:
# 1. Observe and cache state handle:
HANDLE=$(curl -s https://api.hanzo.ai/v1/decisions \
-H "Authorization: Bearer $HANZO_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "kai-1.7", "state": {"repo_map": "...", "images": [...]}, "cache_state": true}' | jq -r .handle)
# 2. Query questions against cached state handle:
curl https://api.hanzo.ai/v1/decisions \
-H "Authorization: Bearer $HANZO_API_KEY" \
-H "Content-Type: application/json" \
-d "{
\"model\": \"kai-1.7\",
\"handle\": \"$HANDLE\",
\"questions\": {
\"next_step\": {
\"type\": \"choice\",
\"instructions\": \"Which file should be edited next?\",
\"criteria\": {\"auth.ts\": \"Authentication\", \"db.ts\": \"Database\"}
}
}
}"This dual-memory pattern encodes the long context once into the L0 Global Latent Memory and L1 Local Index, avoiding repeated encode passes on subsequent decisions.
Resources & Source Code
- GitHub Repository: github.com/hanzoai/kai
- Model Card & Benchmarks: github.com/hanzoai/kai/blob/main/MODEL_CARD.md
- Decision Recipes: github.com/hanzoai/kai/tree/main/recipes
- Production Developer Guide: /docs/decisions
- Early Access Request: hanzo.ai/research-access?track=kai