Sentious AI
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Sentious Intelligence

A system of agents.
Under your control.

Specialized agents operate on shared intelligence. Every action passes through explicit human or policy controls, every decision becomes structured learning data, and improvement is measured against actual outcomes.

01

Agentic Architecture

A coordinated system of specialized agents that learn from every decision.

Data Inputs

Video (RGB)
Depth
Audio
IMU / Sensors
Robot State
Text / Documents
Human Annotations
Evaluations
Project Context

Sentious Agent Suite

Annotation

Understand & label data

Quality

Detect issues & improve ground truth

Workflow

Route, prioritize, and automate

Data Intelligence

Find patterns & opportunities

Collection

Plan, monitor & improve capture

Operations

Run projects, workforce & delivery

Learning

Turn decisions into better systems

Shared Intelligence Layer

Memory
Tools
Policy Engine
Decision Data
Model Services
Security & Permissions

Outcomes

Better data
Faster delivery
Lower cost
Continuous improvement
Real-world impact
02

How an Agent Works

The same core loop powers every agent, with domain-specific inputs and actions.

1

OBSERVE

Ingest data and context

2

REASON

Analyze, find patterns, predict, and understand

3

RECOMMEND

Suggest next best actions

4

HUMAN / POLICY

Approve, edit, reject, or set rules

5

LEARN

Capture decisions and improve future behavior

6

IMPACT

Measure and improve

Learning at Multiple Speeds

Context LearningImmediate — Project-specific adaptation
Policy CalibrationMinutes / Hours — Adjust thresholds & routing
Workflow / Guideline LearningHours / Days — Improve instructions & processes
Model LearningPeriodic — Retrain or fine-tune models
03

Annotation Agent

Understands multimodal data and suggests labels.

Multimodal Input

Robotic hand approaching the blue mug
0:12 / 0:32
RGBWrist (L)Wrist (R)DepthForceRobot StateAudio

AI Suggestions

ReachContactGraspAGENT0:12.84 – 0:15.7192%LiftTransportPlace

Evidence

Gripper closureObject motionContact signal38 similar examples
AcceptEditReject

How It Learns

Captures boundary correctionsLearns project-specific definitionsIdentifies common failure modesFeeds training data over time

Impact

Acceptance rate74%86%Boundary corrections21%9%Annotation time4m12s2m48s
04

Review Routing Agent

Predicts risk and routes to the right reviewer.

Input Signals

AI confidence92%Contributor history96%Sensor quality99%Task difficultyLowNoveltyLow

Risk Prediction

Low risk

Routing Decision

Auto-passunder policySpot checkStandard reviewExpert review

Routed automatically
(under policy)

Learning

Learns from false positives & negativesCalibrates by project, task & domainImproves routing policies over time

Impact

Manual review volume38% lowerMissed-error rate62% lowerMedian review latency4h12m1h48m
05

Coverage Agent

Finds gaps and recommends what to collect next.

Current Data Distribution

Objects

Mug28%
Bottle12%
Box8%
Tool6%
Other46%

Environment

Indoor78%
Outdoor12%
Low light4%
Cluttered6%

Failure Modes

Success82%
Slip6%
Drop4%
Misplace3%
Other5%

Gap Detection

[Placeholder]
Reflective metallic mug
Underrepresented

Reflective objects
only 2.4% of episodes

Target: 1,400

Generate Collection Plan

How It Learns

Analyzes dataset compositionLearns from model performanceIdentifies high-value data typesImproves future recommendations

Impact

Reflective-object success63%79%Low-value collection avoided42%Episodes / performance point2.3× more efficient
06

Guideline Agent

Turns human feedback into better instructions.

Patterns in Feedback

“Grasp begins after stable closure”342“First contact is not grasp”287“Wait for object to move”198“Need clearer definition”124

Pattern confidence 94%

Proposed Guideline Update

CURRENT

Mark grasp when the gripper contacts the object.

RECOMMENDED

Mark grasp when stable closure establishes control of the object. Initial contact should be labeled Contact.

ApproveEditReject

How It Learns

Clusters similar feedbackIdentifies systematic correctionMeasures impact after changesBuilds a knowledge base over time

Impact

Published · v4.3
Reviewer disagreement29%11%Correction rate17%9%Contributor confidence72%91%
07

Operations Agent

Monitors delivery, capacity, quality, and operational risk.

Project Status

Dataset

Manipulation v1

Target delivery

Sep 18, 2026

Completed 74%Remaining 26%
Throughput1,120/day
Acceptance92%
Review backlog3,842

Agent Insight

Delivery at riskProjected to miss target by 1.7 days · 80% range 1.1–2.4 days

Key drivers

1.Review backlog growth42%
2.Site B performance31%
3.Acceptance decrease18%
4.Other9%

Recommended actions

Increase review capacity +18%Shift workload to Site ARepair camera station B-07Adjust routing policyApply Plan

How It Learns

Compares forecasts to actualsLearns which interventions workBuilds more accurate estimatesImproves resource allocation

Impact

Forecast accuracy+41%Average delivery delay−62%Operational cost−27%
08

Evaluation Feedback Agent

Connects model performance back to data.

Model Evaluation

Task

Grasping — reflective objects

Reflective objects63%Overall91%

4,820 evaluation episodes · policy-v17

Failure Analysis

Slip after liftMisalignmentFailed initial grasp

Data Insights

Reflective objects represent only 2.4% of training data. Increasing coverage may improve performance.

Recommendation

Collect 1,200 additional reflective-object manipulation episodes.

Create Collection Plan

How It Learns

Correlates failure patterns with dataset compositionRecords proposed data interventionsMeasures model performance after interventionImproves future collection recommendations

Impact

Model success rate63%79%Failure rate↓ 43%Data efficiency+2.1×

The Compounding Loop

Every decision makes the next decision smarter.

People

Annotate, review, and provide feedback

Agents

Understand, recommend, and automate

Decision Data

Capture choices, corrections, and outcomes

Learning

Update policies, guidelines, and models

Better Data

Higher quality, greater coverage

Better Models

Improved real-world performance

Real-World Impact

More capable systems in the real world

A continuous cycle of improvement