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A Salesforce/MuleSoft/Deloitte Digital survey of 1,050 global IT leaders reported accelerating agentic AI adoption, with respondents projecting increased spend and operational complexity: IT leaders expect to allocate ~19% of IT budgets to AI agents, while “agentic AI pioneers” run more software (an average of 1,057 applications vs. 669 for organizations still planning). The report highlighted data integration as a primary blocker (cited by 82% of leaders) and noted that only 27% of enterprise applications are connected, with 71% describing their environments as overly interdependent—conditions that can increase operational risk and complicate security oversight as autonomous systems are introduced.
Separately, a security-focused framework article argued that agentic systems’ autonomy (tool use, cross-system actions, and rapid decision-making) creates new exposure points that traditional controls may not adequately address, calling for visibility, accountability, and permissioning over agent actions. It emphasized the need for “agentic security posture management” approaches that track context and intent, enforce least-privilege for agent workflows, and maintain auditable controls to support regulatory readiness as organizations scale agent-driven automation.
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IT leaders said they planned to devote about 19% of IT budgets to agentic AI initiatives, indicating a significant increase in investment during 2026. The report also found that about half of AI agents still operate in silos, while roughly 40% of respondents were already using interoperability approaches such as A2A and MCP.
The same report found that enterprises manage an average of 957 applications but have connected only 27% of them, with 82% of IT leaders citing data integration as a major obstacle to AI adoption. It also reported that 97% of leaders faced challenges including security, compliance, legal risk, legacy incompatibilities, lack of expertise, and data quality issues.
A 2026 benchmark report by Salesforce, MuleSoft, and Deloitte Digital, based on surveys and interviews with 1,050 global IT leaders, found that AI agents were already used across 89% of teams or functions. Organizations reported an average of 12 agents in use and projected growth to 20 over the following two years.
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Enterprises and public-sector organizations are accelerating adoption of **AI agents** and generative AI to automate knowledge work and software delivery, with guidance increasingly framed as a management and governance problem rather than a purely technical one. Commentary on agentic AI in software development describes agents as autonomous decision loops operating within guardrails (goal decomposition, tool selection, execution, observation, and iteration), enabled by mature CI/CD automation and API-driven infrastructure. Separate reporting highlights empirical findings that AI-generated code has grown to nearly **30%** of code by late 2024 and is associated with an estimated **~4%** productivity lift, with gains concentrated among more experienced developers despite higher usage among less-experienced staff. Security and procurement implications are emerging alongside this adoption. Research on **agentic tool chain attacks** warns that AI agents’ “reasoning layer” and natural-language tool metadata become an attack surface, enabling techniques such as **tool poisoning**, tool shadowing, and “rugpull” behavior that can lead to covert data leakage or unauthorized actions; the risk is amplified when tools are centralized via architectures like the *Model Context Protocol (MCP)*, where compromise of a shared tool server can propagate malicious behavior across many agents. In the US federal context, agencies are signaling demand for AI tools that deliver operational value while meeting requirements for security, transparency, and responsible use, and the General Services Administration is also tightening contractor cybersecurity expectations for work involving **CUI** by requiring alignment with **NIST SP 800-171** (and select **800-172** controls), including MFA, encryption, vulnerability remediation, and removal of end-of-life components, with independent assessments as part of authorization and ongoing monitoring.
Jun 29, 2026
Organizations are rapidly expanding the use of **AI agents**—systems that can execute multi-step tasks with limited human supervision—while governance, safety, and oversight controls lag behind. Deloitte’s *State of AI in the Enterprise* survey of 3,200+ business leaders across 24 countries reported **23%** of companies already using AI agents “at least moderately,” projected to rise to **74%** within two years, while only about **21%** said they have robust safety and oversight mechanisms in place. Separately, commentary warning about AI-enabled intrusion acceleration cited a purported “**GTG-1002**” campaign in which AI agents allegedly automated most of the intrusion lifecycle and compressed response windows, arguing that traditional SOC processes struggle against autonomous, high-velocity adversary tradecraft. Multiple other items in the set focus on broader *responsible AI* and policy concerns rather than a single security incident: an interview-style piece describes how “responsible AI” functions inside a large vendor’s product process, and another report highlights expert concerns about deploying LLM tools in **law enforcement** workflows (e.g., summarizing body camera transcripts or generating crime scene photo descriptions) given risks like hallucinations and bias. A separate business-leadership article frames cybersecurity and AI as strategic imperatives amid geopolitical instability but does not provide incident-specific or vulnerability-specific details. Overall, the material is best characterized as **governance and risk posture** coverage around agentic AI rather than a unified, verifiable breach or vulnerability disclosure.
Jun 29, 2026
Organizations are increasingly deploying agentic AI systems—autonomous software agents capable of making decisions, executing workflows, and interacting with APIs and productivity tools without direct human oversight. These AI agents, powered by large language models and advanced reasoning capabilities, can automate complex business processes such as HR reviews, scheduling, and infrastructure management, but their autonomy introduces new security and governance challenges. Even minor misalignments in agentic AI objectives can result in unintended actions, such as mass communications to unintended recipients, causing operational confusion and reputational risk. The shift from traditional automation to agentic AI means enterprises must address how to secure, monitor, and govern entities that can learn, adapt, and act independently. Unlike static robotic process automation, agentic AI can dynamically adjust to changing conditions, orchestrate actions across diverse systems, and continuously improve its own processes. This unprecedented level of autonomy demands proactive security strategies to prevent unauthorized actions, data leaks, and compliance violations, as well as robust oversight mechanisms to ensure these agents act in alignment with organizational goals.
Jun 29, 2026