Moving agentic AI from innovation theatre to enterprise production
At Malaysia's Ryt Bank, customers can simply tell their banking app what they want to do and an artificial intelligence (AI) agent will queue up the transaction, pausing only for a human confirmation before transferring any funds. Meanwhile in Australia, hardware chain Bunnings has recently developed an AI assistant that provides customers with expert advice and helps them find what they need, transforming the e-commerce experience beyond traditional search. Across the Asia-Pacific region, the enterprise AI conversation has been moving away from generative AI (GenAI) chatbots towards agentic AI, systems that can plan, execute and self-correct to achieve complex business goals.
Agentic AI represents a fundamental shift from AI as a reactive tool to AI as an active digital worker. Unlike traditional chatbots that wait for prompts and generate content based solely on input, agentic systems use reasoning engines to decompose goals, take actions, and adapt when steps fail. This autonomy brings unprecedented efficiency but also introduces new layers of complexity for CIOs tasked with moving these digital workers from controlled sandboxes into live production environments.
Escaping innovation theatre
Despite the hype, broad-scale deployment of agentic AI is still in its infancy. Research from Avanade indicates that 44% of organisations remain stuck at the proof-of-concept (PoC) stage. Many PoCs never reach production because they lack alignment with real business needs. As Sagar Porayil Vadakkinakathu, CTO at Mindsprint, notes, many initiatives are discontinued due to fragmented pilots that lack orchestration, data readiness, and governance. He warns that the biggest mistake enterprises make is treating AI agents as isolated pilots rather than embedding them into the enterprise fabric. Organisations build impressive agents in sandbox environments but fail to integrate them with core systems, data flows, and business workflows. In simple terms, enterprises don't fail because the AI doesn't work; they fail because the supporting ecosystem isn't ready for it.
When operationalised well, however, the results can be rewarding. Mindsprint recently implemented an AI-native deal-to-delivery digital backbone for a global food and agri-conglomerate, embedding agentic workflows into procurement, logistics and trade operations. Similarly, FPT Software deployed AI-powered virtual assistants for a leading Japanese trade company to streamline document handling and translation, achieving a 90% reduction in processing time and up to an 80% drop in error rates. These examples show that agentic AI can deliver tangible value when properly integrated.
The concept of "innovation theatre" — where organisations showcase pilots without real-world impact — is particularly dangerous for agentic AI because it creates a false sense of progress. To avoid this pitfall, enterprises must define clear business outcomes from the start, establish integration roadmaps, and allocate dedicated resources for productionisation. This means moving beyond the data science team alone and involving IT operations, security, and business stakeholders from day one.
Preventing agent sprawl
As enterprises move beyond single-agent deployments to handle more complex workflows, they face a new operational challenge: managing a workforce of digital workers. Without a proper orchestration layer, organisations risk agent sprawl, where disconnected AI agents operate in silos without shared context. Bindu Sunil, chief AI officer at Mindsprint, emphasises that equally important is deep integration with enterprise systems and data. Without access to reliable, real-time data from systems like ERP, an agent's ability to execute workflows breaks down.
Integration means embedding agents directly into the systems where work actually happens. To manage this effectively, Frank Bignone, senior vice-president at FPT Software, advises enterprises to look for solutions that enable agent-to-agent communication. This hierarchical setup allows a manager agent to coordinate the work of multiple specialist agents. At a technical level, orchestration requires strict parameters to prevent autonomous agents from running amok. According to Charlie Dai, vice-president at Forrester, agentic AI platforms must manage task decomposition, state tracking, retries, and — crucially — termination conditions to prevent agents from entering infinite loops. Tool invocation needs validation, schema constraints, and fallback logic. Integration via APIs and events is critical to decouple agents from core systems.
Bhavya Kapoor, president of Avanade Asia-Pacific, adds that orchestration platforms must be policy-aware and provide real-time control. This ensures that oversight happens before agents act, not only after outcomes are audited, turning agent sprawl into a governed, enterprise-ready capability. For CIOs, this means investing in a centralised AI orchestration platform that can monitor all agents, enforce policies, and provide a single pane of glass for governance.
The governance gap
As agents take on execution capabilities, traditional data security measures are no longer enough. Kapoor points out that sovereignty now requires three distinct forms of control: behavioural, operational, and cognitive. He warns that if sovereignty stops at the data layer, enterprises leave the decision-making layer exposed. As AI agents move from assisting work to autonomously executing tasks within core business processes, this gap is no longer theoretical. It is where operational, regulatory and reputational risks accumulate fastest.
This requires a shift from retroactive auditing to proactive, real-time control. Sunil stresses that trustworthy AI is not a philosophy; it is an engineering discipline. She advocates for robust adversarial review systems to stress-test multi-agent failure cascades, where one agent's bad output becomes another agent's corrupt input, along with a constitutional and policy layer. Regulatory requirements, ethical guardrails, and enterprise compliance rules are mapped directly into the AI pipeline as version-controlled, auditable constraints. A data privacy rule becomes an interceptor. A fairness requirement becomes a scoring constraint.
Just as important is the audit and feedback loop. Models drift, regulatory environments shift, and what was within acceptable thresholds at deployment may not be six months later. Every agent action is logged, every decision is traceable, and production signals feed continuously back into the governance layer. The shift this creates is the one that matters most when enterprises face scrutiny: moving from "trust us" to "here's the complete trail." That is where enterprise AI credibility is genuinely won or lost, and it is the difference between a programme that survives its first production incident and one that gets quietly switched off.
Unbounded behaviour and bill shock
With traditional software-as-a-service (SaaS) offerings, software pricing is generally predictable. But with agentic AI systems, an agent might take five steps to resolve an IT ticket, or it might encounter an error and loop 50 times to solve it, racking up token consumption along the way. The cost challenge isn't just about tokens; it's about unbounded behaviour. Kapoor advises CIOs to treat agentic AI like cloud economics by defining clear guardrails upfront, such as iteration limits and token budgets per workflow, and shifting the financial model from open-ended consumption to cost per outcome.
Forrester's Dai echoes this, advising CIOs to model the full AI system cost. Agentic workloads introduce high variability from retries, multi-model orchestration, data access, and tool calls, making simple calculators unreliable. Effective total cost of ownership (TCO) combines token usage with data costs, infrastructure, governance, and operating overhead, with continuous optimisation and hard usage guardrails to prevent bill shock. Organisations should also consider implementing showback or chargeback mechanisms to make business units aware of the costs their agents incur, fostering responsible usage.
Rethinking data and the IT team
A common refrain is that AI is only as good as the underlying data. But must an enterprise's data lake be pristine before an agent can be unleashed? Not exactly, says Kapoor: trust isn't built on pristine data; it's built on controlled environments. What matters is having clear ownership, consistent standards, and strong controls over sensitive and business-critical data.
To manage this complex environment, the shape of the IT department is also changing. The role of the standalone prompt engineer is already being viewed as too narrow. Managing agentic AI is less about crafting clever prompts and more about ensuring AI systems are reliable, governed, integrated into core workflows, and aligned to business outcomes. Dai notes that prompt engineering, together with context engineering and harness engineering, are not standalone roles. Long-term success depends on teams that can redesign workflows, encode business policy, govern risk, and continuously adapt agents in production. This means CIOs should invest in cross-functional teams that combine data engineering, AI/ML expertise, security, and business process knowledge.
Repositioning the human worker
As AI agents begin executing tasks on behalf of human employees, particularly in areas such as software engineering, customer service, and IT operations, change management is becoming a critical success factor. Employees are understandably cautious. Resistance increases when agents act without transparency or recourse. Employees accept execution agents faster when they retain oversight and see reduced toil rather than job displacement.
Kapoor points out that organisations getting this right treat AI adoption as a people transformation, not just a tech roll-out. Agentic AI doesn't remove humans from the equation; it repositions them. Employees shift from performing tasks to overseeing, guiding, and improving systems, elevating the nature of work rather than diminishing it. Ultimately, the true measure of agentic AI's success is moving away from simplistic metrics such as headcount reduction towards outcome-based measures that reflect how work improves. Most organisations focus on productivity gains, operational efficiency, and process quality improvements, such as reduced cost to serve, faster cycle times, and better decision accuracy. Dai notes that while hours saved matter, reliability, adoption, and trust indicators are equally important. Durable success is defined by sustained usage in production with stable costs and declining human escalation over time.
Source: ComputerWeekly.com News