Process Optimisation and Parameter Tuning with Agentic AI

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  

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: 

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 

• 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 

• 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.