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Dynatrace's latest global study, The Pulse of Agentic AI 2026, highlights the importance of observability and reliability in the operationalisation of agentic AI. The research surveyed 919 senior leaders in the field, revealing insights into the implementation challenges and opportunities faced by enterprises.
The study highlights a key trend. While enterprises remain committed to AI, they encounter hurdles due to governance, validation, and scalability concerns of autonomous systems.
Findings show that around 50% of projects are still in the Proof-of-Concept or pilot stage. Despite the early phase, adoption is climbing rapidly; 26% of organisations report having eleven or more projects. As they transition from experimentation to full-scale deployment, there is an increasing demand for platforms that are not only reliable and trustworthy but also extensively validated.
Budget allocations reflect this ambition with 74% expecting increases for the coming year. This shift signifies a structural transition where reliability becomes essential to enterprise readiness for agentic AI.
Budget increases are on the horizon, with nearly half the leaders predicting an increase of over $2M. The most common deployments are in IT operations and DevOps (72%), software engineering (56%), and customer support (51%).
Top priorities include:
The highest ROI is anticipated in ITOps/system monitoring, cybersecurity, and data processing. However, security, privacy, compliance concerns, and technical challenges remain significant barriers.
Human oversight remains integral even as organisations aim for greater autonomy. Though 64% utilise a blend of autonomous and supervised agents, only a fraction of decisions (13%) are made by fully autonomous agents.
The top validation techniques include: data quality checks, human review of outputs, and anomaly detection.
As agentic AI scales, observability becomes a key intelligence layer, providing visibility across all phases—from development to operationalisation. Its adoption is notably high during implementation (69%), operationalisation (57%), and development (54%).
Currently, 70% of organisations employ observability to glean real-time insights into agent behaviour and decision-making. Notably, the majority still utilise agentic AI for a mix of internal and external applications.
A definitive statement from the report echoes: "Observability not only helps teams understand performance and outcomes, but it provides the transparency and confidence required to scale agentic AI responsibly and with appropriate oversight."
