AI agent implementation introduces a challenge in complex industrial environments: as organizations scale their agent-based systems, managing proliferating codebases while maintaining consistent governance becomes increasingly difficult.

Subject matter experts who deeply understand operations can’t directly shape agent behavior without coding skills, while development teams struggle to maintain oversight and consistent standards across growing implementations.

This disconnect between operational expertise and technical implementation limits the practical value of agent-based systems in large organizations. The path forward requires rethinking how we implement AI agents to put operational knowledge and control at the center, rather than code.

The concept of parametric design has transformed various engineering fields since the 1960s by separating what something does from how it’s controlled. From architecture to manufacturing, parametric approaches have consistently proven more effective than hard-coded solutions for complex systems.

This same principle now offers a powerful solution for AI agent implementation. Instead of embedding operational knowledge in code, parametric agents use well-defined parameters to create bounded, governable intelligence that can adapt within clear operational limits.

The Implementation Challenge

Code-based Agentic frameworks like Microsoft AutoGen, LangGraph, and CrewAI present significant challenges for agentic solutions in complex industrial operations. While these tools offer powerful capabilities, they create several fundamental problems that become more apparent as implementations scale:

• Domain experts can’t directly embed their knowledge without going through developers
• Different teams implement agents with varying standards and controls
• Changes require code modifications across multiple systems
• Scaling becomes increasingly difficult as complexity grows
• Governance remains a constant challenge

The impact of these limitations becomes more apparent as organizations try to scale their agent implementations. What works for small proof-of-concept projects often fails when applied to complex industrial operations.

The Power of Parameters Over Code

The solution lies in a fundamentally different approach to agent implementation. Instead of embedding rules in code, parametric agents separate what an agent can do from how it should operate. This separation makes all the difference in practical implementation and long-term sustainability.

A parametric agent is a software entity that combines domain-specific parameters and constraints with generative AI capabilities to operate within clearly defined boundaries while maintaining contextual awareness and adaptive decision-making abilities.

Think about how experienced operators run complex equipment. They don’t follow rigid scripts – they work within clear operational boundaries while adapting to changing conditions. Parametric agents work the same way, combining clear limits with intelligent adaptation. This approach preserves operational wisdom while enabling consistent improvement.

XMPro MAGS Prompt Management with Parameters

The key difference lies in how operational knowledge gets captured and applied. Traditional coding requires translating expert knowledge into programming languages – a process that often loses crucial details. Parametric agents allow experts to define operational boundaries directly, preserving their understanding of how systems should work.

The Parametric Control Framework

Parametric agents implement a multi-layered control framework that ensures consistent operation across all levels of the organization. This structured approach provides clear boundaries while enabling appropriate autonomy at each level:

Team-Level Parameters

• Resource allocation and coordination rules
• Collective performance objectives
• Cross-agent communication protocols
• Team-wide operational constraints

Profile Parameters

• Role-specific capabilities and limitations
• Standard operating procedures
• Default behavioral settings
• Security and access controls

XMPro MAGS Agent Profile (Template) Parameters

XMPro MAGS Agent Profile (Template) with additional Parameters

Instance Parameters

• Location-specific configurations
• Equipment-specific constraints
• Local operating conditions
• Performance thresholds

XMPro MAGS Agent Instance with Parameters

Objective Function Parameters

• Clear performance metrics
• Value optimization targets
• Risk management thresholds
• Compliance requirements

Each layer of parameters works together to create a comprehensive control framework that adapts to local conditions while maintaining organizational standards.

The parametric approach enables organizations to create templates for Agent Profiles, Teams, and Instances that can be rapidly deployed across operations without additional coding. This centralized approach also ensures that when business rules or compliance requirements change, updates automatically flow to all agents using those profiles, maintaining consistent governance throughout the system.

The Governance Challenge of Coded Frameworks

The fundamental governance challenges of coded frameworks become more apparent as implementations scale. The situation mirrors the proliferation of Excel spreadsheets in many organizations – just as spreadsheets can multiply across departments with varying quality and control, coded agents can be created and modified in individual development environments without central oversight. This leads to several critical risks:

• Inconsistent implementation standards
• Unclear security boundaries
• Difficult version control
• Limited governance oversight
• Complex testing requirements
• Challenging quality control

The need for coding expertise compounds these challenges by creating bottlenecks and introducing potential points of failure. Every change requires developer intervention, making it difficult to maintain consistent quality or respond quickly to operational needs.

The Parametric Advantage

Parametric agents address these fundamental challenges through structured control and clear governance. Key advantages include the following:

Centralized Control

• All agent behavior defined through verified parameters
• Consistent implementation across the organization
• Clear audit trails for all agent actions
• Standardized deployment and updates

Bounded Autonomy

• Clear limits on agent capabilities
• Explicit authorization levels
• Controlled adaptation within bounds
• Verifiable compliance

I have previously written on “Bounded Autonomy” as a pragmatic response to concerns about fully autonomous AI agents. This approach ensures that agents can adapt to changing conditions while maintaining compliance with organizational requirements.

Real Benefits of the Parametric Approach

For technical leaders, the parametric approach delivers distinct advantages:

• Reduced implementation complexity through parameter-based configuration
• Clear governance through centralized control frameworks
• Simplified compliance with built-in boundaries
• Efficient updates without code modifications
• Scalable architecture with consistent standards
• Reduced technical debt through structured implementation
• Verifiable security through explicit constraints

Business leaders gain equally important benefits:

• Direct configuration by subject matter experts
• Faster deployment without coding delays
• Lower operational risk through bounded autonomy
• Consistent operations across the organization
• Sustainable scaling without governance compromise
• Clear accountability through comprehensive audit trails
• Preserved domain expertise through parameter capture

Looking Forward

The future of AI agents requires balancing powerful capabilities with proper control. Parametric agents provide this balance by enabling subject matter experts to shape AI behavior while maintaining clear operational boundaries directly. Unlike coded approaches that risk uncontrolled proliferation, the parametric framework ensures that agent implementation remains both powerful and properly governed.

Organizations that adopt parametric agents position themselves to scale AI capabilities sustainably while preserving the crucial domain knowledge that drives operational excellence. The focus shifts from writing code to capturing expertise, creating a path to agent implementation that truly serves business needs.

In my next article, I will explore how the combination of Value Engineering, Objective Functions, and this Parametric Agent approach deliver measurable business outcomes at scale.

---

Pieter van Schalkwyk is the CEO of XMPro, specializing in industrial AI agent orchestration and governance. Drawing on 30+ years of experience in industrial automation, he helps organizations implement practical AI solutions that deliver measurable business outcomes while ensuring responsible AI deployment at scale.

---

About XMPro: We help industrial companies automate complex operational decisions. Our cognitive agents learn from your experts and keep improving, ensuring consistent operations even as your workforce changes.

---

Our GitHub Repo has more technical information if you are interested. You can also contact myself or Gavin Green for more information.

Read more on MAGS at The Digital Engineer