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System Status
Online: 3K+ Agents Active
Digital Worker 9 AI Agents Active

AI Container Designer

Deploys a coordinated team of 9 specialized AI agents that perform parallel analysis across all engineering domains. Uses Chain-of-Thought reasoning with visible decision trails, Multi-Criteria Decision Analysis (MCDA) for material selection, Monte Carlo simulation for cost optimization, and Human-in-the-Loop approval gates for critical decisions.

9 AI Agents
7 Tech Stack
AI Orchestrated
24/7 Available
Worker ID: ai-container-designer

Problem Statement

The challenge addressed

Custom container design projects require weeks of manual analysis across material science, structural engineering, quality, cost, sustainability, and regulatory compliance. Engineers must coordinate across departments, perform repetitive calculations...

Solution Architecture

AI orchestration approach

Deploys a coordinated team of 9 specialized AI agents that perform parallel analysis across all engineering domains. Uses Chain-of-Thought reasoning with visible decision trails, Multi-Criteria Decision Analysis (MCDA) for material selection, Monte C...
Interface Preview 4 screenshots

Design input wizard with volume & budget configuration, priority weights for AI decision making, and real-time market intelligence panel showing resin pricing and supply chain status

Multi-Agent Orchestration Engine showing real-time analysis pipeline with Orchestrator and Material Scientist agents, chain-of-thought reasoning, and inter-agent communication

Human-in-the-Loop approval gate displaying pending manager approval, key findings summary with PCR content, pricing, and process capability metrics

Scenario Execution Summary with completed workflow timeline showing Planning, Market Intelligence, Material Analysis, Structural Analysis, and Regulatory Compliance phases

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

9 Agents
Parallel Execution
AI Agent

Orchestrator Agent

Complex multi-agent workflows require coordination of task decomposition, parallel execution, dependency management, and result synthesis across multiple specialist agents.

Core Logic

Implements ReAct pattern for workflow coordination, decomposes design briefs into parallel agent tasks, manages inter-agent communication via broadcast/delegation messages, synthesizes outputs from all specialists into coherent recommendations, and handles approval gate escalations. Uses Task Planner and Agent Router tools.

ACTIVE #1
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AI Agent

Material Scientist Agent

Selecting optimal materials from 47+ polymer options requires balancing FDA compliance, barrier properties, processability, cost, and sustainability requirements - a complex multi-criteria decision.

Core Logic

Executes MCDA (Multi-Criteria Decision Analysis) across all material options weighted by customer priorities. Queries material database with barrier requirements, validates FDA 21 CFR 177.1520 compliance, calculates sustainability scores, and generates weighted rankings. Outputs primary material recommendation with alternatives and decision rationale.

ACTIVE #2
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AI Agent

Structural Engineer Agent

Container structural integrity requires Finite Element Analysis (FEA) for stress/strain analysis, drop test simulation, stacking capacity validation, and mold complexity assessment.

Core Logic

Runs FEA simulation with mesh node analysis based on container geometry, calculates wall thickness optimization, predicts drop test results at specified heights, determines stacking capacity, assesses mold complexity (cavity count, cycle time), and estimates tooling costs. Outputs safety factors and structural recommendations.

ACTIVE #3
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AI Agent

Quality Analyst Agent

Predicting manufacturing quality outcomes (Cpk, defect rates, yield) before production requires statistical process capability analysis and test prediction modeling.

Core Logic

Calculates process capability indices (Cpk) using Statistical Process Control (SPC) methods, predicts Six Sigma levels and PPM defect rates, generates control limits (UCL/LCL), predicts outcomes for drop tests, seal integrity tests, and stacking tests. Recommends SPC monitoring strategies and quality improvement targets.

ACTIVE #4
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AI Agent

Cost Optimizer Agent

Accurate cost estimation requires modeling uncertainty in material costs, volume impacts, market volatility, and identifying optimal price points across multiple scenarios.

Core Logic

Executes 1,000-iteration Monte Carlo simulation for probabilistic cost modeling, generates multi-scenario pricing (low/target/high volume), calculates unit cost breakdowns (material, molding, decoration, QC, overhead), performs sensitivity analysis on key cost drivers, determines break-even volumes and payback periods.

ACTIVE #5
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AI Agent

Sustainability Analyst Agent

Sustainability assessment requires lifecycle carbon footprint calculation, recycled content verification, recyclability assessment, and alignment with corporate environmental targets.

Core Logic

Calculates carbon footprint per unit and annually using LCA methodology, validates PCR/PIR content against targets, assesses recyclability percentage and rating, computes circular economy scores, identifies certifications achieved (FDA, EU 10/2011, ISCC PLUS eligible), and quantifies environmental impact metrics (CO2 saved, trees equivalent, plastic diverted).

ACTIVE #6
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AI Agent

Risk Validator Agent

Validating recommendations requires cross-checking agent outputs for consistency, identifying risks across technical/supply chain/quality/regulatory/market categories, and ensuring confidence levels are justified.

Core Logic

Implements autonomous Reflection Loops for self-validation - questions confidence scores, gathers evidence from multiple agents, adjusts confidence based on market volatility or data quality issues. Identifies and categorizes risks with probability/impact scores, generates mitigation strategies, and validates material-process compatibility.

ACTIVE #7
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AI Agent

Market Intelligence Agent

Design decisions require real-time market context including current resin pricing, PCR premiums, supply chain risk levels, and competitive positioning.

Core Logic

Queries real-time market data for material pricing and PCR premiums, monitors supply chain disruptions and risk indices, performs competitor benchmarking against Berry Global, Sonoco, Pactiv Evergreen, detects industry trends, and communicates pricing volatility insights to Cost Optimizer agent for model accuracy.

ACTIVE #8
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AI Agent

Regulatory Compliance Agent

Container designs must comply with FDA, EU, and state regulations (CA SB 54) which have different requirements for food contact materials, migration limits, and recycled content mandates.

Core Logic

Validates FDA 21 CFR 177.1520 compliance for food contact applications, verifies EU 10/2011 migration limits, assesses California SB 54 (RPPC) 2024 targets for recycled content, identifies ISCC PLUS certification paths for mass balance tracking, generates compliance documentation packages for customer review.

ACTIVE #9
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

Enterprise-grade multi-agent orchestration system for custom rigid plastic container design feasibility analysis. Coordinates 9 specialized AI agents through an 11-phase workflow including planning, market intelligence, material analysis, structural engineering, regulatory compliance, quality prediction, cost optimization, sustainability assessment, agent collaboration/consensus, risk assessment with reflection loops, and synthesis with executive report generation.

Tech Stack

7 technologies

RxJS reactive state management with BehaviorSubjects for real-time agent status updates

Chain-of-Thought (CoT) reasoning pattern implementation with visible thinking steps

Tool-calling architecture with typed ToolExecution interfaces

Human-in-the-Loop (HITL) approval workflow with ApprovalGate system

Agent memory system (short-term, long-term, episodic) for context retention

ReAct pattern (Reasoning + Acting) for autonomous agent behaviors

Multi-agent collaboration protocol with consensus building

Architecture Diagram

System flow visualization

AI Container Designer Architecture
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