Cisco is expanding Webex Contact Center to support what it describes as the agentic era of customer experience. Its strategy brings together autonomous AI agents, real-time assistance for employees, AI-powered workforce management, persistent customer context, and integrated security and observability.
The objective is broader than automating individual interactions. Cisco wants to help enterprises coordinate complete customer journeys across AI agents, human employees, communications channels, and back-office systems. If successful, this could enable organizations to increase availability and productivity while maintaining the governance and customer experience required for production deployments.
To learn more, Zeus Kerravala and I interviewed Vinod Muthukrishnan, Vice President and General Manager of Webex Customer Experience at Cisco. See the full video below.
Moving From Transactions to Relationships
Many early contact center AI implementations focused on discrete tasks such as checking an account balance, changing a reservation, or issuing a refund. These use cases can deliver value, but they do not constitute a complete agentic customer experience.
Vinod Muthukrishnan described AI’s progression from “a toy to a tool to actually a digital coworker.” In his view, the next step is for AI to move beyond completing transactions and begin coordinating customer relationships. That distinction matters. A transactional bot interprets an immediate request and follows a defined process. A more advanced agentic system understands why the customer is contacting the organization, retrieves relevant information, coordinates actions across multiple systems, and applies policies appropriate to that customer and situation.
The enterprise opportunity is to create more consistent and personalized experiences without requiring customers to repeatedly explain their circumstances. However, the greater the AI agent’s autonomy and access, the greater the potential operational and security risk.
Turning Contact Centers Into Context Centers
Cisco’s AI Concierge vision is designed to maintain context across channels, business systems, AI agents, and human handoffs. Muthukrishnan characterized the goal as ensuring that “there is no wrong door.” Regardless of whether a customer calls a store, messages the contact center, interacts with an AI agent, or speaks with a back-office employee, the conversation should continue with the appropriate context.
This reflects a broader industry shift from contact centers to what Cisco calls “context centers.” Customers do not view separate departments, communications platforms, and databases as independent entities. They see one brand and expect it to remember their previous interactions. Cisco believes its combination of unified communications as a service, contact center as a service, and communications platform as a service provides some of the underlying connectivity required to unify these touchpoints. Its agentic context engine is intended to add a real-time knowledge layer that can make relevant customer information available to either a human or digital agent.
The potential business benefits include fewer repetitive conversations, faster resolution, lower customer effort, and more personalized engagement. Realizing those benefits will depend heavily on data quality, system integration, privacy controls, and the accuracy of the context delivered to agents.
Organizations do not need to connect every customer-facing system before getting started. They can begin with focused use cases such as after-hours service, unanswered store calls, conversation summaries, or routine requests. The important architectural consideration is whether those projects can eventually connect to a broader platform rather than becoming another collection of isolated AI tools.
Improving Human Performance Alongside Automation
Cisco’s strategy does not assume that AI will replace every contact center employee. Instead, the company is using a common AI foundation to support both autonomous interactions and human agents.
Real-time assistance can transcribe conversations, retrieve relevant knowledge, recommend responses, summarize calls, and automate post-interaction tasks. This allows employees to spend less time navigating systems and more time applying empathy, judgment, and problem-solving skills. As Muthukrishnan noted, “Great superlative experiences are delivered often by humans.” That observation is especially relevant as organizations decide which interactions to automate and which require human expertise.
The best division of labor will vary by customer journey. Routine, high-volume requests may be well suited to AI, while sensitive or complex issues may continue to benefit from human involvement. AI can still support those human-led interactions by reducing administrative work and presenting relevant information at the appropriate moment.
Managing a Blended Workforce
Cisco’s AI Native Workforce Platform extends workforce engagement to both human and digital workers. This recognizes that AI agents also have capacity, quality, cost, and skills considerations. Although digital agents don’t require conventional shifts, their use generates compute and token costs. Organizations must decide which interactions AI is authorized to handle, how much capacity to allocate, and when to transfer a customer to a person. They must also evaluate whether automation improves or degrades the experience.
Traditional measures such as average handle time, first-contact resolution, and customer satisfaction remain relevant, but no single metric tells the complete story. Reducing handle time means little if customers must call again or leave dissatisfied. Similarly, a high containment rate may appear financially attractive while masking poor experiences.
Enterprises should evaluate AI and human performance by comparable customer outcomes while accounting for the different complexity of the interactions each handles. The operational objective should be to balance cost to serve with resolution, customer effort, satisfaction, and availability.
Autonomy Requires Accountability
As AI agents gain access to customer information and permission to execute transactions, security and governance must become part of the deployment architecture rather than a later addition. Muthukrishnan summarized the issue clearly: “Autonomy without accountability is a liability.”
Cisco is positioning AI Agent 360 as an integrated environment combining agent development with AI Defense and AI Agent Observability. The intended lifecycle includes examining connections to knowledge sources and Model Context Protocol servers, testing agents before deployment, monitoring runtime behavior, detecting prompt injection or hallucinations, and using telemetry to improve performance.
This integration could be a meaningful differentiator for Cisco, given its broader security and observability portfolio. However, enterprises should validate how consistently these capabilities operate across Cisco and third-party environments. Most large organizations will use multiple models, applications, data sources, and AI platforms.
Cisco’s support for Model Context Protocol, agent-to-agent communications, and bring-your-own-AI options therefore warrants attention. Openness will be important because customer experience extends well beyond any single vendor’s portfolio.
Why It Matters
The next phase of contact center transformation will not be defined solely by how many interactions an organization automates. It will be determined by whether AI and human expertise can be combined to improve resolution, availability, consistency, and customer satisfaction at a sustainable cost.
Enterprises should establish an ambitious long-term vision but begin with a measurable use case. They should define customer and financial success metrics, test security and governance controls, study employee and customer responses, and then scale what works.
Cisco’s direction reflects the market’s movement from isolated copilots and bots toward governed systems of AI. The strategy is promising, but execution will require disciplined integration, reliable context, openness, and continuous measurement. For enterprise leaders, the priority should not be deploying the most AI. It should be applying AI where it creates demonstrably better customer and business outcomes.
