Agentic AI revolutionises enterprise software with autonomous decision-making and governance challenges

The shift from passive AI copilots to autonomous, agentic systems is transforming enterprise operations, but raises vital questions about security, transparency, and trust to ensure sustainable adoption.

Generative AI is undergoing a profound transformation, shifting from providing passive assistance to becoming active collaborators capable of making decisions, executing complex tasks, and interacting autonomously within enterprise systems. What began as AI copilots off...

ering suggestions and insights is evolving into agentic AI, autonomous systems that perceive, decide, and act with minimal human oversight. This transition stands as one of the most significant developments in enterprise technology, opening new opportunities while raising critical questions about governance, security, and trust.

Traditionally, AI in enterprise settings functioned primarily as informers or assistants, augmenting human capability without independent action. However, AI agents now extend far beyond these roles. They can understand user intent expressed in natural language, formulate multi-step plans, learn continuously from feedback and context, simulate human-like reasoning in uncertainty, and interact across diverse applications and APIs. This evolution corresponds with expert forecasts: Gartner predicts that by 2028, approximately one-third of enterprise software will embed agentic AI, fundamentally altering software development and operational workflows.

The implications for software engineering are profound. According to Stanford’s AI Index, AI’s task performance has doubled roughly every seven months since 2019, mirroring Moore’s Law but in the cognitive domain. This acceleration means development tasks once taking months, coding, testing, deployment, can now be completed in days or hours as AI agents dynamically orchestrate complex processes. Consequently, the developer’s role is shifting from hands-on execution to higher-level intent-setting, governance, and orchestration, heralding the era of the Hybrid SDLC (Software Development Life Cycle) where humans and AI agents co-create software.

This new landscape is giving rise to a new professional archetype: the Agentic Engineer. Unlike traditional coders or machine learning specialists, Agentic Engineers specialise in designing intelligent delivery systems, managing feedback loops, and orchestrating agent behaviour across environments. They focus on architecture, governance, and setting goals while AI agents automate tasks across the entire software lifecycle, from code generation and testing to deployment and monitoring. Platforms like Sanciti AI exemplify this shift, providing enterprise-grade agentic AI solutions that automate complex processes at scale while embedding governance and compliance to ensure secure and efficient operation.

Nonetheless, this leap in autonomy introduces significant risks alongside its benefits. The increased independence of AI agents raises pressing questions about accountability, transparency, and control within enterprise environments. How can organisations verify what actions an agent took and the rationale behind those decisions? Are the agents’ activities secure, explainable, and compliant with emerging regulatory frameworks? How do enterprises manage data and tool access by these agents, and ensure that ‘zombie agents’, autonomous agents left running without oversight, do not create security vulnerabilities?

To address these challenges, industry thought leaders emphasise the need for a robust System of Record for AI Agents. Such a system acts as a unified, persistent ledger that treats agents as integral participants in the software supply chain. It tracks agent-generated assets, code, configurations, prompts, test outcomes, credentials, maintains an immutable audit trail of decisions and actions, and preserves contextual metadata for behaviour monitoring. This infrastructure supports regulatory compliance by embedding governance into the agents’ workflows, controlling their lifecycle safely from onboarding to deactivation. Just as the open-source software movement propelled the demand for secure supply chains, agentic AI necessitates equally stringent artifact and behaviour management to ensure the technology’s reliability and safety at scale.

Despite the promising trajectory of agentic AI, adoption faces hurdles. A Gartner report highlights that over 40% of current agentic AI projects are expected to be discontinued by 2027 due to high costs and uncertain business value. The market is also plagued by ‘agent washing,’ where vendors exaggerate capabilities by branding conventional AI tools as agentic without genuine autonomous function. Consequently, enterprises and technology providers must pursue validated use cases, enforce guardrails, limit uncontrolled autonomy, and maintain strong oversight to realise sustainable and trustworthy deployments.

When successfully integrated, agentic AI promises substantial gains. Gartner forecasts that by 2029, these systems will autonomously resolve 80% of common customer service issues, reducing operational costs by around 30%. Such autonomy will reshape customer interactions, automating routine tasks while freeing human agents to manage more complex concerns.

However, as SAS and other industry voices note, balancing autonomy with human oversight remains vital for ensuring ethical standards, data privacy, and regulatory compliance. Trustworthy, explainable AI decisions underpin this balance, necessitating transparent governance frameworks that prevent unchecked autonomous behaviours.

Ultimately, the future of enterprise software lies in the marriage of speed and accountability. Organisations that prioritise building not only cutting-edge AI models but also the infrastructure for trust will lead the next wave of innovation. These agentic systems must be dependable, secure, transparent, and compliant, not just intelligent and fast. It is in this synergy of autonomy and governance that agentic AI will deliver transformative value while safeguarding enterprise integrity.

Source: Noah Wire Services