98%
of device-activation workflows automated
US fiber-powered telecom serving 150,000+ households — automation layered on top of existing workflows, not swapped in for them.
Why we bet on agentic AI over RPA
RPA and agentic AI solve different problems. What separates them shows the moment something on the path changes — a shifted field, a new rule, a reformatted document.
Where RPA fits
RPA is still the right tool — until the path changes.
In its article "From RPA to agentic AI: rebuilding the revenue cycle," Vstorm described what a well-run RPA deployment actually does. A bot is configured per payer, per workflow. It logs into a portal, runs an eligibility check, files a request, posts a payment, or submits a status inquiry. Within those boundaries, it is fast, deterministic, and consistent for every case, every time.
Agentic AI does not change any of that. For stable, high-volume, rules-based transactions, RPA remains the right tool, and no amount of added reasoning capability argues for retiring automation that already works. The question worth answering is not whether to replace RPA. It is where RPA's model stops being sufficient — and what agentic process automation adds on top of it.
The limitation is structural, not a matter of quality. An RPA bot follows a fixed path. When anything on that path changes — a UI field shifts, a document is reformatted, a new rule appears — the bot breaks. Each break either halts the bot or pushes the case into a manual exception queue. The queue does not resolve itself: every case sitting in it still needs a person to close it, which is where rework cost accumulates.
RPA
Agentic AI
Executes a fixed rule, configured per workflow — locked to the process exactly as it was defined when the bot was built.
Trigger
Goal-driven reasoning. It plans, retrieves information from multiple sources, and works out what the case actually needs today.
Breaks. A shifted UI field, a reformatted document, or a new rule stops the bot on its fixed path.
When the input changes
Adapts. It reasons through cases that rules-based automation cannot, instead of stopping at them.
A manual exception queue. Every case still needs a person to close it — rework cost that does not resolve itself.
Recovery
Autonomous reasoning through the exception, without dropping the case to a human queue by default.
Stable, high-volume, rules-based transactions — where it remains fast, deterministic, and consistent.
What it is for
The work beyond a fixed rule's reach. It layers on top of RPA, rather than swapping working automation out.
The distinction
The difference that matters is not speed. RPA executes a rule. Agentic AI reasons about an exception — it plans, retrieves, adapts when an input changes, and works through the cases fixed-path automation runs out of road on.
Two production deployments, one pattern
What layering looks like at scale
A US fiber-powered telecom serving 150,000+ households shows the layering in production: a multi-agent architecture — one orchestration agent plus five specialized sub-agents for devices, network, accounts, troubleshooting, and documentation.
Breaking a complex workflow into specialized, narrow-role components reduces the hallucinations a monolithic agent would be prone to. Mixam, a printing company, needed a product advisor for 1 billion+ product combinations — a multi-agent system on Pydantic AI and RAG, with an 11.76% increase in orders on day 1 of the Australian launch.
10x
reduction in error-processing time
The same telecom deployment: heavier models run only where nuance is actually needed, controlling cost and latency.
95.4%
workflow success rate — Mixam
Guardrails constrain the agent to Mixam's real, live product options, pulled via API, so it cannot recommend something unprintable.
In practice
How the layering actually works
Three structural choices behind moving beyond a fixed rule's reach — without giving up what RPA already does well.
01
Layered, not rip-and-replace
RPA keeps running the stable, high-volume, rules-based transactions it already handles well. Agentic AI layers on top, for the work beyond a fixed rule's reach. Treating it as a wholesale replacement introduces risk without buying back anything RPA was already delivering.
02
Multi-agent, not monolithic
One orchestration agent plus specialized sub-agents, each with a narrow role. Splitting a workflow this way reduces the hallucinations a monolithic agent is prone to, and lets heavier models run only where nuance is needed — controlling cost and latency.
03
Humans keep the decision
Layering agentic AI onto a governed workflow raises the oversight bar, not lowers it. Agents operate under least-privilege access to the systems they touch, every action is observable and auditable, and escalation paths keep a person on the consequential decisions — in the telecom and printing deployments above, the agent compresses the work that precedes a decision without removing the decision-maker.
The boundary agentic AI respects
The pattern holds outside the revenue-cycle context that prompted it. A separate Vstorm deployment for a US healthcare provider serving 100,000+ members automates pre-appointment scheduling across multiple channels — a distinct problem from the revenue cycle itself, but the same underlying logic: reasoning layered on top of existing automation, not swapped in for it.
"An RPA bot does what you told it to do last quarter. An agent works out what the payer is asking for today." — Vstorm perspective
The agent compresses the work that precedes a decision. It does not remove the decision-maker. That is the boundary agentic AI is built to respect, not erase — which is also why Vstorm layers it onto RPA rather than betting one against the other.
Where to go deeper: the orchestration pattern behind both deployments is set out on multi-agent system development, the printing deployment is written up in full in the Mixam case study, and the guardrails that keep a reasoning agent inside its brief are the subject of Part 3 of this series.
See where your fixed-path automation runs out of road
Talk to Vstorm about layering agentic AI on top of the automation you already have — not replacing it.
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More in this series
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