How to Design and Govern AI Agents at Scale
13 Oct 2025
Discover key strategies, design principles, and governance practices for building and scaling AI agents responsibly in enterprise environments.
Artificial intelligence (AI) has rapidly become the backbone of innovation for businesses in fast-paced, competitive industries. SMEs, especially founder-led enterprises in the UK aiming for rapid growth, are on the frontline of this transformation. But as AI evolves, it brings challenges beyond technology - how do you build, scale, and govern AI agents responsibly while maintaining operational efficiency?
In an engaging interview on the "OnCloud podcast", Gary Aurora hosts Brent Collins, Vice President of Enterprise AI Strategy at Intel, to explore the intricacies of designing and managing scalable AI systems. This article distils their key insights into practical, actionable strategies for SMEs.
Introduction: Moving Beyond AI Hype
While the AI conversation often centres on massive, all-encompassing systems, the real potential for enterprises lies in specialised AI agents - independent, task-focused models that collaborate to achieve broader objectives. Collins compares this to how humans function in the workforce, with generalists and specialists working in concert. Understanding this nuanced approach can help SMEs leverage AI more efficiently, focus on results, and avoid unnecessary complexity.
The future of AI for SMEs isn’t about chasing the biggest models or the flashiest solutions. It’s about strategic innovation, governance, and alignment with business operations. Let’s break this down.
Section 1: The Role of Specialised AI Agents
The AI landscape often presents two conflicting visions: a single, powerful artificial general intelligence (AGI) versus a constellation of smaller, specialised agents. According to Collins, these two paths aren’t contradictory - they’re complementary.
He draws an analogy with healthcare, where a general practitioner coordinates care, while specialists tackle specific areas. Similarly, businesses don’t need a "mega-model" AI to solve every problem. Instead, they can deploy focused AI agents for tasks such as:
Streamlining niche operational processes.
Automating repetitive tasks like booking systems.
Enhancing customer service with domain-specific chatbots.
Why SMEs Should Focus on Specialisation
Cost-Efficiency: Smaller, specialised models consume fewer resources, making them more accessible for businesses with limited budgets.
Targeted Problem-Solving: These agents are designed to tackle specific business challenges, ensuring precision and effectiveness.
Scalability: Modular systems allow businesses to expand capabilities incrementally without overhauling existing workflows.
Key takeaway: Don’t fall into the trap of thinking bigger is better. SMEs should prioritise using the right AI tool for the right task.
Section 2: Designing Responsible AI Architectures
Building an effective AI system isn’t just about choosing the right models - it’s about developing a robust architecture. Collins emphasises the need for an orchestrator to manage workflows between independent agents, translating human intent into actionable tasks.
Key Non-Negotiables for AI Design
Standards: Adopt evolving industry standards to ensure compatibility and interoperability across systems.
Governance: Prioritise privacy, security, and data integrity. For example:
Data Labelling: Ensure data is correctly classified and has consistent metadata for tracking.
Chain of Custody: Monitor how data flows between agents to prevent errors or privacy violations.
Real-Time Data Management: Implement checkpoints to ensure data accuracy and timeliness, especially for fast-changing fields like finance or supply chains.
Addressing Compliance Challenges
SMEs often struggle with data federation - managing data across compliance boundaries like departments, legal zones, or geographies. Collins recommends:
Building systems with built-in compliance checkpoints.
Regularly updating and replacing outdated data to avoid atrophy.
Training AI systems to assess both data quality (accuracy) and relevance (timeliness).
Key takeaway: Governance and standards aren’t optional. They’re critical for building trust and avoiding legal or reputational risks.
Section 3: Balancing Innovation and Risk
According to Collins, businesses should embrace innovation but balance it carefully with risk management. Early adoption may involve taking calculated risks, but as the technology matures, rigorous security measures must follow.
Mitigating Risks in Agent Autonomy
Autonomous AI agents bring new threats, including:
Prompt Injection Attacks: Manipulating AI outputs through malicious inputs.
Decision Loops: Agents making flawed choices in echo chambers.
Data Leakage: Sensitive information unintentionally shared across systems.
To address these risks:
Introduce human checkpoints at critical decision-making stages.
Invest in monitoring systems to detect and rectify anomalies early.
Maintain a "fail-fast" mindset - test, learn, and iterate while risks are manageable.
Key takeaway: SMEs must strike a balance between innovation velocity and security to avoid runaway risks while staying competitive.
Section 4: Operational Models for AI at Scale
AI adoption isn’t just a technical shift - it’s an organisational one. As Collins points out, the way you manage AI agents will resemble managing employees, requiring a complete rethink of operations.
Adapting Operational Models
Value Stream Mapping: Analyse workflows to design optimal AI implementation, avoiding the mistake of automating inefficient processes.
Agent-Centric Governance: Treat AI agents as decision-makers with accountability. Ensure policies are in place for versioning, monitoring, and updating agents.
Infrastructure Overhaul: AI can strain existing infrastructure. For example:
Data centres may require upgrades to handle increased power consumption and cooling needs.
SMEs should assess whether they need GPUs, CPUs, or specialised silicon, based on workload requirements.
Key Consideration for Hardware Decisions
CPU vs GPU vs AI Silicon: Avoid overinvesting in niche hardware. Start with workload requirements, then evaluate whether existing systems suffice or new investments are needed.
Key takeaway: Future-proofing your operational model is just as critical as adopting the right technology.
Key Takeaways
Specialised AI Agents are the Future: SMEs should focus on deploying targeted models for specific tasks rather than relying on large, generalised systems.
Governance is Crucial: Privacy, data integrity, and real-time accuracy must be top priorities.
Strike a Balance Between Risk and Innovation: Embrace early AI adoption but establish clear safeguards as systems mature.
Redesign Operational Models: Managing AI agents will require new workflows, governance structures, and potentially upgraded infrastructure.
Invest Smartly in Hardware: Let workload requirements drive your decisions on CPUs, GPUs, or specialised silicon.
Be Open to Change: AI offers opportunities to rethink traditional workflows - don’t just replicate old processes with new tools.
Conclusion: Democratisation of Intelligence
Collins concludes the conversation with a powerful vision: "What if everyone knew what everyone knew?" AI has the potential to democratise expertise, making deep knowledge and skills accessible to all. From improving operational efficiency to enhancing work-life balance, the future of AI promises a workplace that is not only more productive but also more human-friendly.
For SMEs in the UK, now is the time to think big. By embracing specialised AI agents, prioritising governance, and aligning technology with business goals, enterprises can scale effectively while staying competitive in an ever-evolving market.
AI isn’t just a tool for large corporations; it’s an enabler for SMEs to innovate boldly, operate efficiently, and redefine their industries. The future begins now - are you ready to lead?
Source: "Designing and governing AI agents at an enterprise scale" - Deloitte US, YouTube, Aug 28, 2025 - https://www.youtube.com/watch?v=z4crkj6hg_Y
Use: Embedded for reference. Brief quotes used for commentary/review.