Uncover how Cisco reworked buyer help 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 instances, we’ve unified intelligence throughout cloud, on-premises, and sovereign cloud environments to ship proactive steering 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 steering and AI-driven help get delivered immediately contained in the buyer’s workflow. My perspective is formed by a singular vantage level: I’ve additionally served as a technical decide 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 techniques throughout the {industry} has confirmed our strategy.
Our mission: improved in-product expertise
Buyer expertise is at all times a high precedence at Cisco. We observed a recurring subject we name the “three-F downside:” fatigue, friction, and frustration. When issues occurred, clients needed to depart the product, change between a number of portals, repeat their context, and piece collectively assist from completely 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 steering, 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 help, with clean escalation to human engineers wherever clients are. The identical intelligence seems throughout product UIs, Cisco.com, and help workflows, with out requiring each workforce to rebuild the identical scaffolding.
To ship constant experiences, intelligence must be accessible throughout product UIs, Cisco.com, and help 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 help case information, with roughly 1.7 million buyer instances flowing by way of Cisco’s help group yearly. That information is operationalized fairly than archived, feeding immediately into what the AI surfaces in actual time, inside the product, in the meanwhile a buyer wants it.
Utilizing exterior insights to tell inside technique
Cisco’s personal operational expertise drove the architectural selections described in the remainder of this text. The exterior alerts mentioned beneath usually are not the supply of these selections. They’re an unbiased examine on them.
1. Intelligence in isolation vs. actual world context
Our early experiments revealed a recurring downside: techniques confirmed sturdy intelligence in isolation, by way of superior fashions and polished demos, however struggled in real-world situations as a result of they lacked understanding of person intent, system standing, operational limits, and lifecycle context.
Reviewing tons of of exterior submissions later confirmed the identical sample at {industry} scale, which confirmed the architectural course we had already taken.
This strengthened 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, person habits, and product alerts right into a shared operational view.
When a problem arises, the system proactively affords steering, explains the influence, solutions questions, and helps repair the issue, all throughout the product.
2. Scaling AI help throughout enterprise workflows
Our early inside experiments confirmed {that a} single monolithic agent struggled to keep accuracy, explainability, and belief as workflows grew to become extra advanced. Reviewing exterior submissions confirmed the identical sample industry-wide: there isn’t a single, generic AI assistant in a position to 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 proper professional as an alternative of counting on one assistant to deal with every part. This permits the system to progress by way of investigations step-by-step, protect context throughout brokers, and hand off cleanly when human experience is required.
At the moment, the system is delivered as a set of specialised AI brokers working collectively fairly than competing for management. At the beginning of the workflow, a Case Administration Agent gathers the help bundle, logs, and model information with out the shopper 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 evaluates recognized vulnerabilities and advisory applicability in opposition to the shopper’s setting 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 techniques 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; tons of of beforehand fragmented workflows now run by way of a standard expertise layer, saving improvement effort and making certain consistency.
The consequence was not only a higher buyer expertise, however a extra sustainable technique to scale innovation internally.
The influence 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% sooner than conventional help paths. For engineering, this consolidation has streamlined tons of of beforehand fragmented workflows and given product groups a unified basis they not need to rebuild per launch.
Sudden wins
Prospects shared, “Finally, I can present you what’s occurring as an alternative of struggling to clarify it.”
The surprising perception was not that straightforward options win, however that clients most wished a low-friction technique to switch context, to point out fairly than describe, to the human on the opposite finish. Display screen recording occurred to be essentially the most direct mechanism that delivered that. The lesson generalized: the highest-value AI functionality is commonly no matter cleanly captures and transfers context between people, enhancing communication on each side fairly than changing it.
Essentially the most beneficial factor AI can do shouldn’t be at all times essentially the most advanced – 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 shouldn’t be validation — it’s calibration. Reviewing tons of of exterior techniques sharpened our understanding of which design decisions maintain up underneath strain and which quietly fail over time.
At the moment, 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 help portals. The patterns that knowledgeable this strategy have additionally been acknowledged externally: a Gold Stevie, three Silver Stevies, the 2026 Edison Gold Award, the 2026 CODiE Award for Finest Information Administration and Search Resolution, and the ISSIP Influence to Enterprise Service Innovation Award for the in-product expertise platform.
By holding one foot in exterior analysis and the opposite in inside execution, we’ve moved from reactive help to proactive, in-product experiences that scale with the enterprise.
Assets
