Uncover how Cisco remodeled buyer assist right into a context-aware, in-product expertise that now reaches greater than 250,000 customers throughout our core portfolio. By leveraging a decade of operational expertise and roughly 1.7 million annual buyer circumstances, we have now unified intelligence throughout cloud, on-premises, and sovereign cloud environments to ship proactive steerage and seamless escalation.
Over the previous a number of years as a Principal Engineer at Cisco, I’ve helped architect Cisco’s in-product expertise, designing how context-aware steerage and AI-driven assist get delivered immediately contained in the buyer’s workflow. My perspective is formed by a novel vantage level: I’ve additionally served as a technical choose for greater than 500 AI, safety, and enterprise know-how submissions throughout {industry} awards, hackathons, and requirements boards. This weblog explores what we constructed at Cisco and how evaluating methods throughout the {industry} has confirmed our method.
Our mission: improved in-product expertise
Buyer expertise is all the time a high precedence at Cisco. We observed a recurring challenge we name the “three-F downside:” fatigue, friction, and frustration. When issues occurred, prospects needed to go away the product, change between a number of portals, repeat their context, and piece collectively assist from totally different sources.
To deal with this, we created a unified expertise layer that scales throughout cloud, on-premises, and sovereign cloud environments. This layer delivers contextual tooltips, inline steerage, banners, proactive alerts, guided walkthroughs, and an embedded AI assistant that may name specialised sub-agents and set off proof seize with out leaving the product. This basis led to Cisco’s in-product expertise and AI-driven assist, with easy escalation to human engineers wherever prospects are. The identical intelligence seems throughout product UIs, Cisco.com, and assist workflows, with out requiring each staff to rebuild the identical scaffolding.
To ship constant experiences, intelligence must be accessible throughout product UIs, Cisco.com, and assist workflows, throughout cloud, on-premises, and sovereign cloud environments. It’s a unified functionality that few different distributors provide at this scale. It’s also grounded in additional than a decade of technical assist case information, with roughly 1.7 million buyer circumstances flowing by way of Cisco’s assist group yearly. That information is operationalized moderately than archived, feeding immediately into what the AI surfaces in actual time, inside the product, for the time being a buyer wants it.
Utilizing exterior insights to tell inner technique
Cisco’s personal operational expertise drove the architectural choices described in the remainder of this text. The exterior alerts mentioned beneath are usually not the supply of these choices. They’re an unbiased examine on them.
1. Intelligence in isolation vs. actual world context
Our early experiments revealed a recurring downside: methods confirmed sturdy intelligence in isolation, by way of superior fashions and polished demos, however struggled in real-world eventualities as a result of they lacked understanding of consumer intent, system standing, operational limits, and lifecycle context.
Reviewing lots of of exterior submissions later confirmed the identical sample at {industry} scale, which confirmed the architectural route we had already taken.
This bolstered a core architectural choice at Cisco: intelligence alone wouldn’t repair the expertise. Context needed to be handled as a first-class concern.
The answer: Unifying operational view
This conviction formed how we designed our in-product expertise to function as a steady, context-aware system. We unified telemetry, consumer habits, and product alerts right into a shared operational view.
When a problem arises, the system proactively affords steerage, explains the affect, solutions questions, and helps repair the issue, all inside the product.
2. Scaling AI assist throughout enterprise workflows
Our early inner experiments confirmed {that a} single monolithic agent struggled to keep accuracy, explainability, and belief as workflows grew to become extra complicated. Reviewing exterior submissions confirmed the identical sample industry-wide: there isn’t any single, generic AI assistant capable of work throughout enterprise workflows.
The answer:
Exterior alerts validated our perception: autonomy with out construction doesn’t scale. So, we constructed specialised brokers that work collectively, directing customers to the suitable knowledgeable as a substitute of counting on one assistant to deal with all the things. This enables the system to progress by way of investigations step-by-step, protect context throughout brokers, and hand off cleanly when human experience was required.
At present, the system is delivered as a set of specialised AI brokers working collectively moderately than competing for management. At first of the workflow, a Case Administration Agent gathers the assist bundle, logs, and model information with out the client needing to connect them manually. A Configuration Evaluation Agent scans the configuration for invalid statements, rule conflicts, and version-dependent mismatches. A PSIRT and Discipline Discover Agent consider recognized vulnerabilities and advisory applicability towards the client’s surroundings and entitled units. When the workflow must escalate, the system delivers a TAC-ready case with logs, configs, and reasoning hint already hooked up, so the engineer on the opposite finish begins with full context.
3. Designing for operations, not simply demos
Inside Cisco, we prioritized stability, reuse, and consistency over fast characteristic sprawl from the beginning. Exterior evaluations later highlighted the identical divide we had been navigating between methods constructed for demonstration and people designed for dependable operations.
The answer:
By specializing in operational habits from the beginning, we offered guided workflows throughout merchandise and portals that now lead customers by way of fixes and ship proactive alerts.
For engineering, this decreased duplication; lots of of beforehand fragmented workflows now run by way of a standard expertise layer, saving growth effort and making certain consistency.
The outcome was not only a higher buyer expertise, however a extra sustainable strategy to scale innovation internally.
The affect has been measurable. Greater than 25,000 buyer workflows are delivered by way of in-product self-service each week, with decision occasions 25 to 30% quicker than conventional assist paths. For engineering, this consolidation has streamlined lots of of beforehand fragmented workflows and given product groups a unified basis they not must rebuild per launch.
Surprising wins
Clients shared, “Finally, I can present you what’s occurring as a substitute of struggling to elucidate it.”
The surprising perception was not that easy options win, however that prospects most wished a low-friction strategy to switch context, to indicate moderately than describe, to the human on the opposite finish. Display screen recording occurred to be probably the most direct mechanism that delivered that. The lesson generalized: the highest-value AI functionality is usually no matter cleanly captures and transfers context between people, enhancing communication on each side moderately than changing it.
Essentially the most invaluable factor AI can do will not be all the time probably the most complicated – it’s usually about capturing context cleanly so people on both finish can talk successfully.
Takeaways and looking out forward
The worth of exterior analysis will not be validation — it’s calibration. Reviewing lots of of exterior methods sharpened our understanding of which design selections maintain up below stress and which quietly fail over time.
At present, our in-product capabilities attain greater than 250,000 customers throughout Safe Firewall, Wi-fi LAN Controller (WLC), Cisco XDR, SD-WAN, Safe Entry, E-mail Risk Protection, and Cisco.com and assist portals. The patterns that knowledgeable this method have additionally been acknowledged externally: a Gold Stevie, three Silver Stevies, the 2026 Edison Gold Award, the 2026 CODiE Award for Finest Data Administration and Search Resolution, and the ISSIP Affect to Enterprise Service Innovation Award for the in-product expertise platform.
By retaining one foot in exterior analysis and the opposite in inner execution, we’ve moved from reactive assist to proactive, in-product experiences that scale with the enterprise.
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