Conference Description
Key Takeaways
- Two-day conference dedicated to enterprise-scale agentic AI orchestration and autonomous systems deployment
- Addresses critical challenges in scaling AI agents from prototype to production environments
- Covers multi-agent systems, observability, inference optimisation, security, and governance frameworks
- Features case studies from Instacart, Amazon, GM, Intuit, Visa, and Stanford researchers
- Designed for CTOs, Chief AI Officers, product leaders, and infrastructure engineers at large enterprises
- Emphasises independent analysis and practitioner-led discussions over promotional content
Introduction
VB Transform 2026 convenes technology decision-makers, engineers, and product leaders working at the frontier of autonomous AI systems. The two-day conference, held at Hotel Nia in Menlo Park, California, focuses specifically on the orchestration of agentic AI at enterprise scale—a domain that has rapidly evolved beyond experimental chatbots into complex, multi-step autonomous processes capable of executing business-critical workflows with minimal human intervention.
The timing reflects a significant inflection point in enterprise AI adoption. Organisations that spent recent years piloting conversational AI and retrieval-augmented generation systems now face fundamentally different challenges: how to coordinate multiple AI agents working in concert, how to maintain observability across autonomous decision chains, and how to secure systems that can take consequential actions without human approval at every step. These operational realities demand architectural approaches and governance frameworks that most enterprises are still developing.
About VB Transform 2026
The conference programme combines main stage presentations, closed-door executive roundtables, and hands-on workshops. This structure reflects the event’s positioning toward practitioners actively building and deploying agentic systems rather than those seeking introductory overviews. Networking opportunities include dedicated breakfast and luncheon sessions alongside an evening reception, providing extended time for peer discussions outside formal programming.
Case studies from organisations including Instacart, Amazon, General Motors, Intuit, and Visa anchor the programme in operational reality. Stanford researchers contribute academic perspectives on emerging techniques and theoretical foundations. This blend of industry implementation experience and research insight addresses a common gap in enterprise AI events, where content often skews toward either abstract research or narrowly vendor-specific use cases.
Agentic Orchestration and Multi-Agent Architecture
Central to the conference agenda is the challenge of orchestrating multiple AI agents that must collaborate, delegate tasks, and resolve conflicts autonomously. Unlike single-model deployments where inputs and outputs follow predictable patterns, multi-agent systems introduce emergent behaviours that can be difficult to anticipate, debug, or explain. Enterprises deploying these architectures must design coordination protocols, establish clear boundaries for agent authority, and implement fallback mechanisms when autonomous processes encounter edge cases.
The orchestration layer sits between individual agents and the broader enterprise environment, managing context sharing, task routing, and resource allocation. Getting this layer right determines whether agentic systems deliver genuine productivity gains or create new categories of operational risk. Sessions addressing this topic explore both architectural patterns and the practical trade-offs organisations encounter when moving from single-agent prototypes to production multi-agent deployments.
Data Pipelines and Context Management
Agentic AI systems are only as capable as the context they can access. The conference examines how enterprises construct data pipelines that feed relevant, timely, and appropriately scoped information to autonomous agents. This involves more than connecting agents to databases—it requires thoughtful design of context layers that determine what information agents can see, when they can see it, and how that information is refreshed as business conditions change.
Data readiness for agentic AI differs substantially from data readiness for traditional analytics or even earlier generations of machine learning. Agents that take autonomous actions based on stale or incomplete context can cause downstream problems that propagate before human operators notice. The governance implications extend beyond data quality into questions of data lineage, access controls, and audit trails that can reconstruct why an agent made a particular decision.
Observability, Operations, and Evaluation
Traditional application monitoring assumes that software behaves deterministically—the same inputs produce the same outputs. Agentic AI systems violate this assumption fundamentally. An agent given identical prompts on consecutive days might take different actions based on subtle variations in retrieved context, model state, or the outputs of other agents in the system. This non-determinism creates observability challenges that existing monitoring tools were not designed to address.
Conference sessions on this topic explore emerging approaches to tracing agent reasoning, evaluating autonomous decisions against business objectives, and detecting drift in agent behaviour before it causes operational incidents. The evaluation problem is particularly acute: how do organisations measure whether an agentic system is performing well when success criteria may be subjective, context-dependent, or only apparent over extended time horizons?
Inference Infrastructure and Compute Optimisation
Running agentic AI at enterprise scale imposes substantial infrastructure demands. Multi-agent systems may invoke large language models dozens or hundreds of times per user interaction, with each inference call consuming compute resources and adding latency. The economics of agentic AI depend heavily on optimising this inference layer—selecting appropriate model sizes for different tasks, caching intermediate results, and routing requests efficiently across available compute capacity.
Platform engineering teams face decisions about whether to run inference on-premises, in public cloud environments, or through managed API services. Each approach carries different cost structures, latency characteristics, and data residency implications. The conference addresses these infrastructure trade-offs with attention to both technical performance and operational cost management.
Security, Identity, and Governance Frameworks
Autonomous AI agents introduce attack surfaces that security teams are still learning to defend. Prompt injection attacks can manipulate agent behaviour in ways that bypass traditional access controls. Data exfiltration risks increase when agents have broad permissions to access and act upon enterprise information. The identity question—how to authenticate agents, scope their permissions, and audit their actions—requires extending identity and access management frameworks designed for human users and traditional software services.
Governance considerations extend beyond security into regulatory compliance, ethical boundaries, and organisational accountability. When an autonomous agent makes a consequential decision, who bears responsibility? How do organisations demonstrate to regulators that agentic systems operate within acceptable parameters? These questions lack settled answers, making peer discussion among practitioners particularly valuable.
Who Should Attend
VB Transform 2026 is designed for senior technology professionals with direct responsibility for AI strategy, architecture, or implementation. Chief Technology Officers, Chief AI Officers, Chief Data Officers, and Heads of Product Engineering represent the primary executive audience. Infrastructure engineers, data engineers, and AI engineers working on agentic systems will find technical depth in workshop sessions and roundtable discussions.
Industry representation spans retail, financial services, automotive, enterprise software, and cloud platforms—sectors where agentic AI adoption is most advanced and operational stakes are highest. The conference assumes familiarity with foundational AI concepts and focuses on challenges that emerge specifically at enterprise scale and in production environments.
Industry Context
The enterprise AI landscape has shifted markedly since the initial wave of generative AI enthusiasm. Organisations that rushed to deploy conversational interfaces discovered that chatbots, while useful, captured only a fraction of potential value. The next phase—agentic AI capable of executing multi-step workflows autonomously—promises greater impact but demands correspondingly greater sophistication in architecture, operations, and governance.
This transition from pilot projects to production systems defines the current moment. Enterprises are moving past proof-of-concept demonstrations toward deployments that must be reliable, secure, and cost-effective at scale. The challenges are no longer primarily about whether AI can perform useful tasks but about how organisations can operationalise that capability responsibly. VB Transform 2026 addresses this operational frontier directly, providing a forum for practitioners navigating these challenges to share approaches and learn from peers facing similar problems.