GenAI is undergoing a rapid transformation.
What began as simple Copilots, tools designed to assist with recommendations and insights, has quickly evolved into a new breed of AI: Agents that can autonomously make decisions, execute complex tasks, and collaborate across platforms with minimal human direction. This shift presents an opportunity for a watershed moment in enterprise technology. It offers massive opportunities, but also introduces serious risks to how software is built, governed, and trusted.
From Support to Autonomous Action
This leap – from passive assistants to active collaborators – enables AI to tackle complex, multi-step processes. Near-term, Agents will work under human oversight; over time, they’ll operate independently, interacting with users, systems, and other Agents. Gartner predicts that by 2028, 33% of enterprise software will embed Agentic AI: software that perceives, decides, and acts toward goals autonomously.
According to Stanford’s AI Index, AI’s task performance has doubled every seven months since 2019, echoing Moore’s Law, but for cognitive work. In software engineering, that means tasks that once took months could now take days – fundamentally changing how software is built, delivered, and maintained. The human role is increasingly shifting from execution to intent-setting, orchestration, and oversight.
Unpacking the Layers of Agentic AI
While often used interchangeably, AI Agents and Agentic AI describe different layers of this paradigm shift:
AI Agents are autonomous or semi-autonomous systems that:
- Understand user intent through natural language
- Generate structured, step-by-step plans to achieve goals
- Learn continuously from feedback, context and past experiences
- Simulate human-like reasoning in uncertain or open-ended scenarios
- Access APIs, Apps, and Services via a Model Context Protocol (MCP), translating instructions into actions
Agentic AI is the broader ecosystem: agent-to-agent collaboration, coordination across systems, and the architecture that enables multi-agent workflows. It reflects a shift from isolated tasks to coordinated autonomy at scale.
AI agents are emerging as independent actors within the modern SDLC, making decisions, executing tasks, and reshaping how software is built and managed – demanding a fundamental rethink of governance, infrastructure, and accountability.
The New Partnership: Developers and Autonomous Agents
The rise of agents doesn’t displace developers – it elevates them. We are entering the age of the Hybrid SDLC, where humans and agents co-create software. Developers focus on architecture, governance, and intent-setting, while agents execute and adapt processes across the pipeline.
Agents are no longer confined to code generation. They automate tasks across the full lifecycle: from coding and testing to packaging, deploying, and monitoring. This shift reflects a move from static pipelines to dynamic orchestration.
A new developer persona is emerging: the Agentic Engineer. These professionals are not traditional coders or ML practitioners. They are system designers: strategic architects of intelligent delivery systems, fluent in feedback loops, agent behavior, and orchestration across environments. Like past tech revolutions, this one demands new tools – but this time, the tools are intelligent collaborators.
This collaborative dynamic between humans and AI brings undeniable speed and flexibility, but it also introduces new questions of accountability, transparency, and control.
The Challenge of Trust in Autonomous Systems
With greater autonomy comes greater risk. As AI adoption accelerates, enterprises face new blind spots:
- How do we know what an agent did and why?
- Are outputs secure, explainable, and compliant?
- What data or tools did the agent access?
- Are we meeting regulatory requirements as rules and laws evolve?
These questions cannot be addressed retroactively. Trust must be built in from the start – with auditable systems that monitor every action, input, and output, whether human- or machine-generated. Without strong lifecycle controls, abandoned agents can linger as “zombie-agents,” still connected to live systems and vulnerable to exploitation. As agent autonomy grows, trust, governance, and security aren’t just best practices – they are non-negotiable essentials.
Building the Backbone of AI Accountability
To scale agentic AI safely, enterprises must build more than pipelines – they must build platforms of accountability. This requires a System of Record for AI Agents: a unified, persistent layer that treats agents as first-class citizens in the software supply chain.
This system must also serve as the foundation for regulatory compliance. As AI regulations evolve globally – covering everything from automated decision-making to data residency and sovereignty – enterprises must ensure that every agent action, dataset, and interaction complies with relevant laws. A well-architected System of Record doesn’t just track activity; it injects governance and compliance into the core of agent workflows, ensuring that AI operates within legal and ethical boundaries from the start.
This system should:
- Track all agent-generated assets—code, configs, prompts, test results, credentials
- Maintain audit trails of every decision and action
- Provide contextual metadata for behavior monitoring
- Ensure compliance and lifecycle control across environments
- Support safe onboarding and deactivation of autonomous agents
Much like Open-Source Ecosystems demanded secure software supply chains, agentic AI demands robust artifact and behavior management. Without it, enterprises can’t govern how agents build, operate or even know when they should stop. Agentic engineering isn’t just about what AI can do—it’s about how reliably, securely, and transparently it can do it at scale.
To truly harness the potential of agentic AI, organisations must prioritise the development of robust trust frameworks alongside their technological investments. That trust isn’t just internal, it must extend to regulators, auditors, and legal stakeholders, providing transparent and verifiable records of autonomous system behaviour. The next generation of software will be defined by intelligent platforms with security, transparency, and built for longevity, empowering teams to move boldly into the future with confidence and control.
These systems will define the next wave of software: autonomous, accountable, and engineered for trust. Not just intelligent, but dependable. Not just fast, but safe. Not just new, but built to last.
About the Author
Janne Saarela is Senior Strategist at JFrog. Deliver Trusted Software with Speed. The only software supply chain platform to give you end-to-end visibility, security, and control for automating the delivery of trusted releases. The massively scalable, hybrid JFrog Platform is open, flexible, and integrated with all the package technologies and tools comprising the software supply chain. Organizations benefit from full traceability to any type of release and deployment environment including ML models, software that runs on the edge, and software deployed in production data centers.
