Mulesoft Report: Multi-Agent Adoption in Singapore to Surge 58% by 2027, Governance Remains Top Concern for Organisations

As agent adoption hits critical mass, 96% of IT leaders in Singapore say AI agent success depends on integration across systems

IT leaders are turning to API-driven architectures to connect multi-agents and prevent fragmented infrastructure and shadow AI from stalling innovation

SINGAPORE, 4 June 2026 – The transition to an Agentic Enterprise, where humans and AI agents work together, is gaining momentum as organisations in Singapore currently use an average of 12 agents with the number projected to climb 58% within two years. 

However, Singapore IT leaders face looming orchestration and governance challenges: 50% of agents currently operate in isolated silos versus part of a multi-agent system, resulting in disconnected workflows, redundant automations, and the potential risk of shadow AI. 

To address these issues, the research also uncovered that respondents are turning to API-driven architectures as a unified foundation to connect, orchestrate, and govern multi-agents and drive AI success. 

Key Findings from Salesforce’s 11th Annual Connectivity Benchmark Report:

The Road to Multi-Agents

As adoption hits critical mass, AI agents are no longer experimental — they are becoming the primary driver of enterprise productivity. Singapore IT leaders are focused on using diverse agentic solutions and establishing agent communication protocols to manage their fleet of agents.

• High expectations: 98% of IT leaders say agents already have improved or that they expect them to improve employee experiences, and 95% believe they will free developers to focus on higher-value work.
• Diverse development: On average, organisations report that their existing AI agents were developed through various methods, split across:
  • Prebuilt SaaS agents (35%)
  • Embedded agents within enterprise platforms (32%)
  • Custom-built in-house (33%)
• Protocol adoption: As organisations deploy AI agents, they are actively supporting, or planning to support, a range of standards or protocols to manage and connect them, with high levels of interest in:
  • Agent Communication Protocol (49%)
  • Agent Network Protocol (46%)
  • Agent-to-Agent Protocol (44%)
  • Model Context Protocol (42%)
  • Universal Tool Calling Protocol (35%)

The Orchestration and Governance Gap

A critical orchestration and governance gap is emerging as enterprises race to deploy AI agents everywhere. While adoption is high, the infrastructure supporting it needs to be more integrated to support a multi-agent workforce that can collaborate and securely leverage data from across the enterprise.

• App and agent sprawl: Singapore enterprises are using 1,002 applications on average with only 27% of them integrated together. With integration challenges and agent silos, 92% of IT leaders are concerned that agents will introduce more complexity than value.
• Top hurdles: The primary challenges currently hamper agentic transformation:
  • Integrating siloed apps and data (45%)
  • Resources and budget allocation (44%)
  • Legacy infrastructure or system incompatibility (43%)
• Data barriers: 98% of organisations experience barriers to using data for AI use cases, with 41% identifying outdated IT architecture/infrastructure due to data silos/disconnected systems as a top blocker. 
• Rise of shadow AI: 62% of organisations cite cross-application data governance as a top integration challenge. An estimated 31% of APIs are currently ungoverned, on average, and only half of organisations have a centralised governance framework with formal oversight for their agentic capabilities.

Building a Unified Foundation

To bridge the integration gaps, Singapore IT leaders are moving toward a unified foundation. By using APIs as the “connective tissue,” organisations can transform fragmented AI tools into a cohesive, multi-agent system where agents can safely communicate, share data context, and execute tasks across the entire IT estate.

• Connectivity mandate: 96% of Singapore IT leaders agree that AI agent success depends on seamless data integration across all systems. 
• Architecture shift: 97% of IT leaders agree that AI agent success will require IT architecture to become more API-driven, where APIs are fundamental building blocks for connecting applications, data, and AI across an enterprise.
• Accelerating integration: One-third (33%) of teams are already leveraging APIs to speed up integration across systems.
• APIs for AI: 45% of Singapore organisations are already using APIs to connect and govern AI today.

Multi-Agents in Action

Early AI agent adopters are already demonstrating how a unified foundation can move agents from experimental silos into core business operations where multiple AI systems can work together:

• AstraZeneca, a global, science-led biopharmaceutical company, selected Agentforce Life Sciences for Customer Engagement to help transform its customer engagement globally, fostering stronger relationships with healthcare professionals (HCPs) through data-driven, AI-powered engagement. Extending its composable architecture with MuleSoft Agent Fabric, AstraZeneca will orchestrate internal and external agent actions across field engagement, commercial operations, and different brands and regions, allowing its care teams and AI agents to work seamlessly together.

Perspectives:

• “The most critical challenge facing organisations today isn’t access to AI, but  converting that intelligence into real work. Agents can only be as effective as the data and business logic they’re grounded in, and with half of all agents currently operating in isolated silos, that context is fragmented. MuleSoft addresses this by creating a unified foundation that gives agents the context they need to act with intelligence and accuracy. For organisations moving into the multi-agent era, this is where the real opportunity lies – those that invest in orchestration to connect, govern, and coordinate agents across the enterprise are the ones that will move beyond disconnected pilots to become a genuine Agentic Enterprise,” said Gavin Barfield, Vice President and CTO, Solutions, Salesforce ASEAN.
• “This year’s Salesforce and Deloitte Digital research findings highlight a critical inflection point where organisations must move from simply deploying agents to operationalising them at scale. Success requires reimagining integration strategies to build a foundation that is sustainable and secure. By establishing API-driven guardrails, enterprises can ensure their agentic transformation is ready for the demands of the modern enterprise.”- Kurt Anderson, Managing Director and API Transformation Leader, Deloitte Consulting LLP

More information:

• Download the Connectivity Benchmark Report
• Get more insights on the report from the MuleSoft Blog
• Read how enterprises can prepare for multi-agents
• Learn about the new automated agent discovery capabilities in MuleSoft Agent Fabric
• Dive deep into the technical foundations of MuleSoft Agent Fabric

Methodology:

Salesforce’s 11th annual Connectivity Benchmark Report, in collaboration with Vanson Bourne and with insights from Deloitte Digital, uses survey data from interviews with 1,050 IT leaders across the globe. We conducted this double-anonymous online survey between October and November 2025 across the United States, the United Kingdom, France, Germany, the Netherlands, Australia, Singapore, Hong Kong, and Japan. We ensured that only suitable participants responded to the survey using a rigorous, multilevel screening process. Respondents are all IT leaders with managerial positions or above in an IT department. All respondents work at an enterprise organization (defined as having at least 1,000 employees) in the public or private sector.

The data in this announcement is specifically based on responses from the 100 IT leaders in Singapore.

Please see www.deloitte.com/us/about for a detailed description of their legal structure. 

*AI agents are defined as the newest type of AI that can understand natural language, reason about goals, make decisions, and carry out tasks on its own. Agentic AI can coordinate with people, systems, or even other agents to achieve outcomes without step-by-step human input. Examples: virtual assistants that handle complex support cases end-to-end, AI tools that schedule meetings and send follow-ups, or systems that monitor operations and take corrective actions automatically.