Imagine 2026: The Next Phase of Agentic Automation for the Enterprise

Capability isn't the question anymore. Scale is.

Across our customer base, agentic automation is already running production processes, handling exceptions, and shipping outcomes. What's changed is the ambition: more processes, more functions, more of the operational complexity that defines how enterprises actually run.

Scaling the impact of agentic AI for enterprises takes end-to-end orchestration across agents and enterprise apps — because business-critical processes don't run in silos, they work across multiple steps and multiple apps. It takes precise context that's grounded in your business rules, policies and contracts. And, last but certainly not least, it requires governance and observability.

That's what Imagine 2026 is about.

Enterprises facing increased regulatory scrutiny, data residency rules, and talent shortages now have the means to deploy agentic workflows with confidence and end-to-end control at scale — reducing risk, shortening development cycles, and maintaining full operational control with no gaps or blind spots — and with governance, auditability, and human-in-the-loop controls in lockstep with your orchestration layer.

Here's everything we announced in Dallas. For a personalized deep dive, book a demo or talk to us about your enterprise needs today.

Universal Orchestration: Secure AI agent deployment at scale and faster automation creation

Deploying AI agents securely and at scale is a particular challenge where sensitive data and legacy systems require local control. Our new Universal Orchestration capabilities deliver a unified foundation for enterprises to deploy, govern, and accelerate agentic automations safely and efficiently.

EnterpriseClaw: Scaling agent execution across environments — even where cloud agents can’t operate

Preview now · GA later this year

Running AI agents for automation at scale across diverse enterprise environments — including cloud, on-premises, behind firewalls, and airgapped setups — is complicated by sensitive data, regulatory constraints, and legacy systems. This reality often means that agents must run locally under strict governance. Most cloud-first platforms don’t meet these demands.

The new generation of local AI agents — tools like OpenClaw — can do things previous AI assistants never could. They reason, write and execute code, navigate applications, and work through complex multi-step problems the way a skilled employee would. But today these personal productivity agents only work for one person at a time, on their own machine.

EnterpriseClaw is the layer that changes that. It’s a runtime and governance platform that extends proven distributed execution and centralized control infrastructure to local AI agents. This means that enterprises can deploy, scale, and govern OS-level agents securely across thousands of devices, whether on-premises, cloud, or hybrid environments, while maintaining end-to-end visibility and control.

EnterpriseClaw unlocks new agentic automation scenarios that require local execution and strict data control, in particular for use cases inaccessible to cloud agents, such as on-prem data processing, legacy app automation, and automation within sensitive compliance environments. At the same time, it integrates seamlessly with cloud deployments, enabling orchestration of AI agents anywhere in the enterprise. IT maintains control and visibility across all deployed agents centrally without new complexity.

Capabilities:

• Deploys any agent framework (OpenClaw, LangChain, CrewAI, etc.) across enterprise device fleets with centralized IT governance.
• Runs agents locally in managed containers with secure access to files, applications, browsers, and terminals.
• Leverages the same hybrid distributed runtime architecture that underpins all our automation workloads, optimized for dynamic, reasoning AI agents.
• Supports parallel execution at enterprise scale with centralized policy controls.
• Provides full telemetry, audit logs, credential management, and AI guardrails via existing Control Room infrastructure.

Access EnterpriseClaw in preview today for any model including OpenAI, NVIDIA, and Anthropic.

Automation Anywhere Code: Faster, safer building of enterprise-ready agentic workflows

Preview · GA later this year

Automation development often stalls because translating business needs into executable workflows takes specialized skills and time. This gap between business intent and IT delivery slows down progress.

Automation Anywhere Code lets users express their process intent naturally — whether by text, documents, diagrams, or voice — and automatically creates governed automation plans and artifacts including agents, API tasks, bots, forms, and workflows, with testing and automated verification planned as part of the broader product vision. It coordinates all parts of the automation seamlessly, reducing manual wiring by generating structured plans and assembling automation artifacts from business intent. Soon, this capability will extend to other agentic tools like Claude Code and Cursor via MCP, broadening its impact.

Users can also create dynamic, context-aware interfaces with a no-code, visual UI builder that tightly integrates with the APA platform and Context Intelligence, accelerated by generative “prompt to UI” capabilities to speed solution delivery from months to days.

By enabling business analysts and non-technical users to contribute directly, Automation Anywhere Code reduces CoE backlogs and speeds deployment.

Capabilities:

• Captures intent from natural language, documents, BPMN, or process descriptions and produces a structured plan for automation before building.
• Builds all necessary artifacts — agents, API tasks, bots, forms, workflows — automatically, reducing manual wiring.
• Provides an approval step with inline editing and diff-based change management to ensure safe changes.
• Includes a no-code, visual UI builder for agentic automation front ends, accelerated by generative “prompt to UI” capabilities.
• Integrates with APA platform workflows and Context Intelligence for real-time data and context delivery.
• Supports internal datastore connections and retrieval-augmented generation (RAG).

End-to-end governance & observability: Delivering measurable confidence in enterprise AI execution

AI agents can’t be “set and forget.” Without ongoing quality checks, their performance drifts, causing compliance risks and operational surprises. Manual reviews are slow, inconsistent, and don’t scale.

Our new products deliver centralized process governance to deploy agentic workflows with confidence.

AI Model and Agent Evaluations and Benchmarking: Continuous Quality Assurance

GA now · Additional capabilities in upcoming releases

Enterprises deploying AI agents struggle to know if those agents are actually delivering the right outcomes before and after launch. Manual review of agent conversations is slow, inconsistent, and doesn’t scale, leaving teams guessing about agent performance. But without continuous evaluation, AI agents risk drifting from intended behavior, causing operational surprises and compliance gaps.

AI Model and Agent Evaluations and Benchmarking is an automated quality testing system that continuously scores AI agents across goal completion, tool selection, and trajectory accuracy — before launch and in production — so teams identify issues early and maintain control at scale.

AI Evaluations grades more than just if the task is done, it also evaluates how well it’s done, and whether the right steps were followed. The system uses advanced judgement models with high accuracy validated against industry benchmarks.

Capabilities:

• Automated, continuous assessment of agent behavior, replacing slow manual reviews.
• Measures whether agents achieve the right outcome, use appropriate tools, and follow sensible paths.
• Provides actionable metrics to guide prompt tuning, workflow adjustments, and retraining.
• Monitors for performance drift and regressions after deployment, reducing production risk.
• Enables scalable AI governance with visibility across the enterprise.
• Generates synthetic data to create a validated test case dataset (planned feature).

Process Simulation: A flight simulator for enterprise automations

Preview this summer · GA later this year

Most teams can’t fully test complex business processes before deployment. They rely on spreadsheets or partial environments that miss edge cases. Critical backend systems may only be available late in the cycle, increasing risk and costly rework.

Process Simulation acts like a flight simulator for automations, letting teams safely rehearse end-to-end workflows before going live. This catches failures early, tests all branches — including errors — and speeds change cycles with confidence.

Capabilities:

• Simulate full processes step-by-step, including all branches — success, rejection, fallback, and error paths — without requiring live backend systems.
• AI-driven scenario generation expands test coverage beyond typical happy paths, surfacing gaps and potential failures early.
• Visualize execution outcomes in a safe environment, enabling proactive issue identification and resolution.
• Integrate continuous validation into CI/CD pipelines for ongoing quality assurance as processes evolve.
• Complements AI Evaluations by governing the process logic while AI Evaluations governs agent behavior within steps.

Process & contextual intelligence: Precision context for smarter automation

Preview · GA rollout starting Q3

Generic AI context often brings noise and errors, undermining automation effectiveness. Enterprises need precise context grounded in their policies, systems, and execution history to improve decision accuracy and reduce manual intervention.

Context Intelligence Graph delivers exactly that. Building on the Process Reasoning Engine (PRE), it supplies the right context at every step — agents, automations, and human decisions — drawing from enterprise-specific sources. In early agent-level benchmarks, this precision has improved trajectory accuracy and goal completion.

Capabilities:

• Connects to systems of record, policies, SOPs, and knowledge bases to create a structured, normalized context layer.
• Delivers step-level context relevance, avoiding information overload and ensuring decisions reflect real business rules.
• Enables consistent context across agents and processes, improving handoffs and reducing escalations.
• Supports a “living map” of organizational operations, with compounding intelligence that learns and improves over time (coming late H2).
• Feeds all agents, models, and enterprise systems without vendor lock-in.

New agentic solutions: Purpose-built agentic workflows to accelerate enterprise impact

Over and above our platform announcements, at Imagine 2026 we launched new ready-to-deploy agentic solutions tailored for the way enterprise processes actually run.

Our pre-built solutions for the Autonomous Enterprise are where people, AI agents, automation, and systems work together to run business processes with built-in governance, measurable outcomes, and less manual work, enabling faster adoption and immediate business value.

Autonomous Service Desk: Multi-agent orchestration for self-driving IT support

GA now

Traditional IT service desks face high ticket volumes, slow resolution, and costly licensing and implementation. Most tools focus on ticket deflection rather than true autonomous resolution. Enterprises need scalable, intelligent IT support that understands, reasons, and resolves requests end to end.

Autonomous Service Desk orchestrates domain-specific AI agents and task agents through advanced reasoning, delivering up to 80% auto-resolution of IT requests. It eliminates the SaaS tax — rising costs tied to more tickets, escalations, staff, and per-seat ITSM licenses — and configuration overhead with pre-built agents and conversational setup for rapid deployment. For complex cases, Agent Assist enhances human agent productivity with real-time knowledge and collaboration tools.

Capabilities:

• Multi-agent orchestration engine coordinating AI across IT domains.
• Pre-built agents for common IT tasks with rapid conversational setup.
• Agent Assist integrates with ServiceNow, Zendesk, Salesforce to enhance human agent workflows.
• Real-time recommendations, auto-summarization, and swarm collaboration that help reduce MTTR and agent workload.

Together, these innovations deliver the infrastructure, intelligence, and tooling enterprises need to deploy agentic automation at scale with confidence and control.

Take a tour of all the advancements announced at Imagine 2026 with a personalized live demo or talk to us about your enterprise needs.