Agentic artificial intelligence (AI) is set to fundamentally reshape the structure of enterprise work and commerce. Rather than simply responding to instructions, these agents actively participate in workflows by planning tasks, creating and using tools, correcting their own errors, and pursuing multistep goals autonomously. The result is faster, more adaptive workflows. The emergence of the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol represents a significant technical advance, analogous to what Hypertext Transfer Protocol (HTTP) and Representational State Transfer (REST) did for web services, providing shared mechanisms for interaction, context exchange, and orchestration. Tool integrations that once required months of labor can now be completed automatically.
Without proper organizational constraints, however, this connectivity introduces a new class of risk. Real-world deployment experience in regulated environments demonstrates that agentic systems can lose coherent context mid-workflow, produce confidently incorrect outputs under ambiguous conditions, and fail in ways that are more difficult to detect than traditional software failures. This distributed systems problem is not solved by smarter AI models, but rather by combining orchestration infrastructure and governance frameworks. Process redesign, not automation, is the pathway to production-ready, trustworthy agentic AI systems.
Trajectory of the AI era
OpenAI’s launch of ChatGPT in 2022 marked the beginning of the large language model (LLM) era for large organizations. At that time, most deployed agents were stateless, single-turn systems designed to perform narrow tasks. In 2024, Anthropic released MCP as an open standard for connecting AI systems to data systems. Google followed in 2025 with the A2A protocol, which allows agents to coordinate tasks and share information across multiple platforms. Together, these protocols form complementary layers in the technology stack, accelerating the introduction of agentic AI into enterprise systems.
In 2026, the transition from LLMs to agentic AI represents a technological advance and a paradigm shift in enterprise workflows. Models have evolved from passive responders into active participants in business processes. Teams of AI agents can access multiple enterprise systems and collaborate across them.
With real-time data such as web searches and Internet of Things (IoT) sensor feeds, agents analyze dynamic data feeds, generate insights, and trigger immediate actions. For example, Walmart deployed an autonomous inventory agent that detects demand signals and initiates inventory actions automatically. The results included a 22% increase in e-commerce sales in pilot regions and a significant reduction in out-of-stock incidents.
Another feature that differentiates agentic AI from earlier LLMs is the shift from instruction-based to intent-based computing. Developers can now focus on the “what” rather than the “how” by assigning agents tasks and letting them design new workflows that achieve business objectives. Tools like OpenClaw allow users to give agents broad autonomy, point them toward real problems, and observe how they identify solutions.
According to McKinsey, 62% of organizations are experimenting with AI agents but have not yet deployed them at scale. This gap indicates that the race to adopt agentic AI is still open in ways that technology transitions rarely are at this level of market attention.
Scale relies on orchestration
Companies will close this production deployment gap by designing new orchestration infrastructures. One key challenge in creating these infrastructures is updating state management processes to handle non-deterministic outputs. Adopting A2A and MCP is an essential starting point in this process. These protocols enable the transition from stateless agents, which produce single outputs without retaining transaction history, to stateful agents, which maintain memory of previous tasks and track the status of ongoing processes.
While stateful AI agents offer exciting new capabilities, they require orchestration environments designed with their strengths and limitations in mind. Tomorrow’s industry leaders are asking: “If an agent handled this workflow, how would we redesign the process from scratch?” Anticipating how agents can fail and planning accordingly are critical to this process redesign. The mindset shift from capability-first to failure-mode-first is a clear marker distinguishing mature agentic deployments from ones that create problems at scale.
Scaling agentic AI systems is challenging, which is why it is critical for organizations to start small and learn from quantifiable test cases before tackling more ambitious projects. Clear inputs, distinct transformations, and verifiable outputs are at the core of scalable task architecture. For example, in software engineering, Amazon coordinated agents to modernize thousands of legacy Java applications through Amazon Q Developer, completing upgrades in a fraction of the expected time. This was only possible because Amazon used test suites and structured datasets that enabled software validation. Tasks either passed or failed, allowing agents to evaluate their work and iterate without human intervention.
The financial services company Ramp launched an AI finance agent in July 2025 that reads company policy documents, audits expenses autonomously, flags violations, generates reimbursement approvals, and verifies vendor compliance. These key governance tasks are grounded in verifiable data against which agents can be evaluated, making them auditable and transparent.
Governance frameworks enable speed and trust
MCP and A2A accelerate the adoption of agentic AI in complex, distributed workflows, but without strong oversight, these tools can introduce risks, including unpredictable behavior and security vulnerabilities. In less regulated industries, organizations once struggled to justify the upfront costs of data governance initiatives. Now, these frameworks are exactly what companies need to mitigate risks and scale agentic AI.
The governance-as-multiplier thesis suggests that, in addition to improving transparency and security, strong data governance also increases the speed at which companies can deploy, scale, and profit from agentic AI. According to a 2026 Databricks report, companies that established AI governance frameworks released 12 times as many AI projects as competitors without such policies.
Highly regulated sectors use AI agents to reduce compliance costs and improve reporting efficiency. In telecommunications, for instance, agents detect network anomalies, open service tickets, and alert customers in a single integrated sequence. Service level agreement (SLA) monitoring and reporting, which previously took a human operator 20 to 40 minutes, now executes in under two minutes. As these tangible benefits grow, it is clear that disciplined governance is not a barrier to agentic AI adoption but the foundation that enables its speed, reliability, and scale.
The future of agentic AI depends on infrastructure
AI technology is approaching a new stage of maturity as organizations move from single-turn chatbots to multi-agent orchestration. Shared protocols accelerate this transition through powerful interoperability and new programming paradigms, laying the groundwork for complex workflows in distributed systems.
The technical capabilities of agentic AI are advancing faster than underlying governance architectures. While agentic AI tools are powerful, they still lack transparency and accountability. To address this gap, industry leaders are investing in new orchestration and governance layers that enable agents to reliably collaborate across enterprise systems. There is no simple path to secure, scalable agentic AI. The enterprises that extract the most value from agents are those investing now in infrastructure rather than chasing isolated, high-visibility demonstrations.
About the Author: Santoshkalyan (Tosh) Rayadhurgam is head of advanced AI at a financial services platform. Previously at Meta, he led foundational AI efforts, specializing in building AI models, production-grade AI agents and systems at scale. He has more than 12 years of experience spanning Stripe, Meta, Lyft, and Amazon Lab126. Rayadhurgam holds a master’s degree from Cornell University and a bachelor’s degree from the National Institute of Technology in India. Connect with him on LinkedIn.
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