At its London Symbiosis 4 event on 22 October, Druid AI introduced what it terms Virtual Authoring Teams – a new generation of AI agents that can design, test, and deploy other AI agents. The announcement marks a move towards what the company calls a ‘factory model’ for AI automation.

According to Druid, the system enables organisations to build enterprise-grade AI agents up to ten times faster, and the platform offers orchestration facilities, plus compliance safeguards and measurable ROI tracking. The orchestration engine, Druid Conductor, serves as a control layer that integrates data, tooling, and human oversight into a single framework.

In addition to the Druid Conductor is the Druid Agentic Marketplace, a repository of pre-built, industry-specific agents for banking, healthcare, education, and insurance. With its solutions, Druid wants to make agentic AI accessible to non-technical users, but provide scalability capability suitable for enterprise use.

Chief Executive Joe Kim described it as “AI [that] actually works” – a bold claim in a market flooded with experimentation and unproven automation frameworks.

The new agentic battleground

Druid is not alone in its pursuit. Similar platforms, the likes of Cognigy, Kore.ai, and Amelia, each represent heavy investment in multi-agent orchestration environments. OpenAI’s GPTs and Anthropic’s Claude Projects also allow users to design semi-autonomous digital workers without coding expertise.

Google’s Vertex AI Agents and Microsoft’s Copilot Studio are moving in the same direction, placing agentic AI as an extension to enterprise ecosystems rather than stand-alone products.

The difference between the competing platforms lies in execution – some focus on workflow automation, others on conversational depth or ease of integration with other parts of the IT stack.

For technology buyers, such diversity is an opportunity and a risk. Vendors are racing to define what agentic AI means in practice, and there’s an undoubted element of agentic AI being 2025’s buzzword, implying differentiation between pure LLM models and practical tools useful in business contexts. Some vendors view agentic as an architecture – modular, distributed, and explainable, while others frame agentic AI as a layer of automation that builds itself – or rather, can discover what powers it’s been granted, and use them according to natural language instructions. The truth of agentic AI’s abilities sits somewhere between engineering promises and operational reality.

The business case – and the caveats

Agentic AI systems promise extraordinary benefits. They can accelerate routine development, coordinate multiple business functions, and use data repositories that were once siloed. For enterprises under pressure to deliver digital transformation with limited headcount, the idea of self-building AI teams is compelling.

But the use of the conditional tense in many vendors’ marketing materials and descriptions is telling: agentic AI can achieve savings, could drive faster operations, and so on.

Business leaders should approach such systems with a clear head. There are few proven case studies beyond pilot programmes inside large corporations (those with mature data governance and deep budgets), and even in those organisations, the returns have been uneven. Failures are rarely shouted from the rooftops, after all.

The biggest risks are not technical – they’re organisational. Delegating complex decision-making to automated agents without sufficient oversight introduces potential bias, compliance breaches, and reputational exposure. Systems can also generate automation debt: a growing tangle of interconnected bots that become difficult to monitor or update as business processes evolve.

The issue of necessary organisational change is troubling on two counts, furthermore. Most business processes have evolved a particular way for good reasons, so why change them to implement a new, largely unproven technology? Secondly, what’s often proposed is change that’s instigated by technology implementation. Shouldn’t processes change for strategic reasons, and technology support that change? Is this a case of the IT tail wagging the business dog?

Security remains a further concern. Each agent increases the surface area for potential breaches or data misuse, particularly when they are designed to communicate and collaborate autonomously. As more workflows become self-directed, ensuring traceability and accountability becomes essential, and more difficult to unpick as complexity increases. The necessary headcount to monitor results and ensure rigorous oversight could negate any ROI agentic AI offers.

Why agentic AI attracts enterprises

Despite the challenges, the attraction is easy to understand. A successful agentic system can transform the speed at which an enterprise experiments and scales. By delegating repeatable cognitive tasks – from compliance checks to customer service triage – organisations can redirect human activity elsewhere.

Druid’s Virtual Authoring Teams encapsulate the logic: automate the automation. Its marketplace of domain-specific agents offers enterprises a head start, promising faster deployments and measurable ROI. For sectors struggling with talent shortages and regulatory pressure, that is an appealing prospect.

Moreover, Druid’s emphasis on explainable AI and its orchestration layer suggests an awareness of corporate caution. Its stated pillars – control, accuracy, and results – are designed to reassure boards that transparency can coexist with speed. If the system truly delivers what the company claims, it could narrow the gap between AI experimentation and scalable transformation.

Balancing autonomy with accountability

Still, for every organisation embracing agentic AI, another remains unconvinced. Many enterprises are wary of over-promising vendors and pilot fatigue. A technology capable of designing and deploying its own successors raises operational questions. What happens when an agent acts beyond its creator’s intent? How do governance frameworks keep pace?

Business leaders must treat autonomy as a spectrum, not a goal. The near future of enterprise AI will likely blend human-supervised automation with limited agentic autonomy. Systems like Druid’s may act as orchestration hubs rather than fully independent actors.

From hype to utility

Agentic AI represents a natural evolution of automation in a wild frontier. Its potential is obvious, yet the market still lacks broad, evidence-based validation of sustained business outcomes. It may just be early days, or may be hyperbole drowning out the voices of reason.

For now, agentic systems do work in controlled contexts – contact-centre operations, document processing, and IT service management. Scaling agentic AI across organisations will require maturity not just in technology, but in culture, process design, and methods of oversight.

As Druid and its peers expand their offerings, enterprises will need to weigh the cost of control against the promised wins from better automation. The next two years will determine whether AI factories become a part of business operations, or another layer of abstraction with its own overheads.

(Image source: “Black and grey wolf (female from Druid pack,’Half Black’) walking in road near Lamar River bridge” by YellowstoneNPS is marked with Public Domain Mark 1.0. )

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