Process Optimisation and Parameter Tuning with Agentic AI

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Executive Summary

A leading pharmaceutical manufacturing company faced hurdles in maintaining consistent yields, shortening production cycles, and minimising defects due to manual recipe adjustments and reactive process optimisation. To address these challenges, the company deployed AgentAnalyst.ai and AgentSRE.ai on a context-first agentic infrastructure, enabling AI-Powered Manufacturing Process Optimisation and automated parameter tuning.

Leveraging advanced MES Integration, these Agentic AI Solutions analysed process data from sensors and quality control systems, uncovering correlations between parameters and product outcomes. Real-time production monitoring and intelligent Anomaly Detection triggered automated recipe adjustment recommendations, ensuring continuous optimisation. With seamless ERP Connectivity and industry-grade scalability, optimised recipes were deployed automatically, parameters dynamically adjusted, and production teams notified of recommendations—resulting in measurable efficiency and consistent product quality improvements.

Customer Challenge

Business Challenges

The customer, a global pharmaceutical manufacturer, faced inefficiencies in traditional recipe management, which limited its ability to meet demand.

Key issues included:

Inconsistent yields due to manual parameter adjustments

Extended cycle times and lower throughput

High defect rates and costly rework

Reactive, post-incident optimisation instead of real-time improvement

Business goals:

Improve yield consistency and reduce batch variation

Shorten cycle times to boost throughput

Reduce defects with real-time optimisation

Enable continuous improvement with data-driven insights

Existing solution limitations:

No intelligent correlation analysis between parameters and outcomes

No real-time optimisation during production runs

Poor data integration across quality and process systems

Optimization insights are not scalable across lines

Compliance and pressures:

FDA regulations required quality and documentation consistency

Customer contracts demanded faster delivery and reduced lead times

Technical Challenges

Infrastructure and Systems:

Legacy MES lacked advanced analytics

Data siloed across control systems, labs, and equipment

Manual recipe management with spreadsheets

Technical Debt:

Rule-based controls caused false alarms and missed opportunities

No predictive machine learning models

Outdated historians with limited real-time capabilities

Integration and Data Management:

Different formats from multiple equipment vendors

Weak integration between process, quality, and recipe data

No centralised correlation platform

Scalability and Reliability:

Couldn’t handle complex multi-variable optimisation

Inconsistent performance in high-volume runs

Poor observability into optimisation effectiveness

Security and Compliance

No encryption of sensitive recipe formulations and process data.

Weak access control for critical manufacturing parameters

No audit trail for recipe changes and optimization decisions

Partner Solution

Solution Overview

Figure: High-level Architecture

The company implemented AgentAnalyst.ai and Agent SRE.ai to create an AI-powered process optimisation system.

AgentAnalyst.ai: Analyses historical and real-time process data to identify correlations between parameters and outcomes, recommending optimal recipe adjustments

Agent SRE.ai: Monitors live production parameters, detects deviations from optimal settings, and triggers real-time recommendations

The agents work together to:

Auto-detect correlations between process steps and defect rates

Recommend recipe modifications for improved yield and quality

Monitor live parameters and trigger optimisation alerts

Generate performance reports for continuous improvement

This multi-agent, data-driven system enabled proactive optimisation, reduced cycle times, and maximised production efficiency.

Targeted Industries:

Industry	Use Cases	Value Delivered
Pharmaceuticals (Primary Industry)	Tablet compression, fermentation, and coating processes	Higher yields, reduced cycle times, consistent quality
Chemicals & Petrochemicals	Reactor optimisation, distillation, polymerisation	Improved efficiency, reduced waste, and energy savings
Food & Beverage	Brewing, baking, and mixing processes	Consistent taste, reduced batch variation, faster production
Semiconductors	Wafer fabrication, etching, deposition	Higher yields, reduced defects, improved precision
Automotive	Paint processes, heat treatment, assembly	Quality consistency, reduced rework, faster throughput
Specialty Materials	Polymer processing, composite manufacturing	Enhanced properties, reduced variation, optimised formulations

Recommended Agents

AgentAnalyst.ai → Process data mining, correlation analysis, and recipe optimization recommendations

Agent SRE.ai → Real-time parameter monitoring, anomaly detection, and live optimisation alerts

Solution Approach

1). Data Mining & Correlation Analysis

AgentAnalyst.ai ingests historical process data from MES, SCADA, and quality systems.

Identifies correlations between process parameters (temperature, pressure, time, flow rates) and quality outcomes

Discovers hidden patterns linking specific parameter combinations to defect rates

2) Real-time Monitoring & Optimisation

Agent SRE.ai monitors live process parameters during production runs

Compares current settings against optimised parameter ranges

Triggers alerts when deviations could impact yield or quality

3) Automated Recipe Optimisation

Integrates with MES to automatically update recipe parameters

Suggests process adjustments based on real-time analysis

Documents all changes for regulatory compliance and traceability

Impact Areas

Model

Advanced correlation models reduced parameter optimisation time by 60%

Machine learning algorithms continuously improve with each production batch

Predictive models achieved 92% accuracy in yield forecasting

Data

Unified process, quality, and production data into a single analytical platform

Real-time integration improved decision-making speed by 75%

Historical data analysis revealed previously unknown optimisation opportunities

Workflow

Automated parameter → analysis → optimization → deployment cycle

Reduced manual intervention in process optimisation by 80%
Streamlined recipe management with automated version control

Results and Benefits

Business Benefits:

35% reduction in cycle time through optimised process parameters

28% improvement in yield from data-driven recipe optimisation

42% decrease in defect rates via real-time parameter monitoring

Enhanced regulatory compliance with automated documentation and traceability

Technical Benefits:

Real-time correlation analysis across thousands of process variables

Automated optimisation recommendations during live production runs

99.8% system availability with autonomous error recovery

Complete audit trail for all parameter changes and optimization decisions

Lessons Learned

Data Quality is Critical for Optimization Success
Real-time process optimization requires high-quality, synchronized data from multiple sources. Cleaning and normalizing data streams was essential for accurate correlation analysis.

Domain Expertise Enhances AI Intelligence
Combining process engineers' knowledge with AI insights produced better optimization results than purely algorithmic approaches. Human expertise guided model interpretation and validation.

Start with High-Impact Parameters
Beginning optimisation efforts with the most critical process parameters that directly affect yield provided immediate value and built confidence in the system.

Real-time Feedback Accelerates Learning
Continuous feedback loops between optimisation recommendations and actual results enabled rapid model improvement and increased accuracy over time.

Change Management is as Important as Technology
Adopting AI-driven optimisation required training operators and engineers to trust and act on AI recommendations while maintaining process control authority.

Integration Complexity Requires Planning
Connecting AI agents with existing MES, SCADA, and quality systems needed careful architecture planning and robust API development.

Best Practices Identified

Implement gradual optimisation rollouts starting with non-critical processes.

Establish transparent governance for AI-recommended parameter changes

Maintain human oversight with configurable approval workflows

Use A/B testing to validate optimisation recommendations

Document all optimisation decisions for regulatory compliance

Create feedback mechanisms for continuous model improvement

Future Plans

Expand to Multi-Site Optimisation
Extend process optimisation across global manufacturing sites to leverage enterprise-wide best practices and recipe improvements.

Advanced Multi-Variable Optimisation
Implement sophisticated optimisation algorithms that simultaneously optimise multiple objectives (yield, quality, cost, sustainability).

Digital Twin Integration
Develop digital twins of production processes to simulate optimisation scenarios before implementing changes in live production.

Sustainability Optimization
Incorporate environmental metrics into optimisation objectives to reduce energy consumption, waste generation, and carbon footprint.

Autonomous Recipe Development
Advance toward AI-driven recipe creation for new products based on desired characteristics and historical optimisation learnings.

Conclusion

By deploying AgentAnalyst.ai and Agent SRE.ai, the pharmaceutical manufacturer successfully transformed its process optimisation capabilities. The solution improved yields, reduced cycle times, and enhanced product quality while maintaining regulatory compliance. With AI-powered process optimisation, the company is positioned for continued manufacturing excellence and competitive advantage in the pharmaceutical industry.

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