Why your agentic AI strategy will fail

Everyone’s racing to deploy agentic AI. Salesforce has AgentForce. ServiceNow is all-in on agents, too. And every telco software vendor is promising autonomous assistants that will transform your workflows overnight.

But there’s a failure point nobody sees coming—one that will quietly wreck your AI investments and turn your beautiful agent demos into a multimillion-dollar disaster the moment you scale.

Horizontal agents won’t scale

Salesforce and ServiceNow are brilliant at what they do. But their horizontal platforms have ZERO telco domain knowledge baked in. They’re giving you raw materials and asking you to engineer it all yourself.

Here’s what that looks like in reality. Each agent you build will learn its own interpretation of your terminology based on prompts and trial and error. The sales agent learns one meaning of an “unlimited data plan.” The provisioning agent learns another. The billing agent learns a third. The scary part is that this will work in demos. It will work in pilots. But then something changes—and the entire system fractures.

For example: let’s say you modify your product catalog. Nothing dramatic—just add roaming coverage to your unlimited plan in five new countries. Simple business change.

Now sit back and watch what breaks. If you have chained agents across your entire stack, like a sales agent recommending plans, a provisioning agent activating services, a billing agent calculating charges, and a customer-care agent answering questions, every one of them will need to understand the update exactly the same way.

On a horizontal platform, each agent has learned its own interpretation of “unlimited plan,” “roaming,” and “coverage.” Now you have to manually re-teach dozens—or hundreds—of agents in natural language. Miss one, and a customer gets provisioned for the wrong service. Miss two, and billing miscalculates. Miss three, and you’re explaining discrepancies to the regulators. You won’t know which agent has the wrong understanding until something breaks in production.

And that’s just a simple catalog change.

Now imagine something more serious:

• a roaming-fee cap update in the EU,
• a new KYC requirement in a high-risk market,
• a revised fraud-suspension policy,
• or a network-provisioning rule change for 5G or eSIM.

These changes happen constantly in telco, and every one of them requires perfect multi-agent alignment across sales, care, billing, provisioning, fraud, finance, and compliance.

Without a shared ontology, every agent has to learn about these changes independently—and as a result, will drift independently. That’s not AI at scale. That’s operational Russian roulette.

THIS ISN’T SCALABLE. It’s maintenance hell disguised as automation.

Worse, once agents start taking actions—provisioning SIMs, suspending accounts, applying charges—incorrect reasoning turns into operational failures. 

Agents don’t just think wrong; they do wrong.

Agents expose your technical debt

Horizontal agents shine an unforgiving spotlight on your backend chaos. Every custom field nobody remembers, every brittle integration, every silent business rule living only in human judgment—agents trip over all of it.

Agents magnify every crack you’ve papered over for a decade. And telcos? You don’t have a little of this problem—you have it everywhere.

Vendors will pitch you an agent-orchestration layer to “manage the complexity.” But orchestration doesn’t solve the root problem: each agent still learns your business logic independently. You’re not eliminating chaos—you’re supervising it.

Orchestration organizes the mess. Ontology eliminates it.

Impose logic on your AI

Here’s the real dividing line between AI that works at scale and AI that self-destructs:

Horizontal platforms ask LLMs to learn your business logic.

Ontology-grounded platforms impose your business logic on LLMs.

One is education.

The other is engineering.

Ontology is not a glossary or a data dictionary. It’s the structural truth of your business: what a subscriber is across contexts, how a SIM relates to an IMSI, how products relate to charges, how regulatory rules constrain offerings. It becomes the authoritative semantic model that every agent references.

The difference is stark:

• Without ontology: 50 agents learn 50 different definitions of “subscriber.” Every change requires re-teaching.
• With ontology: 50 agents query one authoritative definition. Update the ontology once and every agent aligns instantly.

Ontology kills “drift” in your agents. Agents stop contradicting each other. They stop looping. They stop deadlocking. They finally act like a team. And because ontology-grounded agents understand operational context—which systems to touch, in what sequence, with what constraints—they don’t just avoid mistakes. They can orchestrate complex workflows autonomously.

Telcos need ontology

No industry is more vulnerable to semantic drift than telecom. For example:

• Our jargon is hyper-specialized. Try explaining ARPU, HLR, or PCRF through prompts.
• Our regulatory constraints vary by country, region, and city.
• Our product complexity explodes combinatorially across add-ons, promos, jurisdictions.
• Our operations are high-stakes: a bad agent doesn’t just give a wrong answer—it misprovisions your network.

Horizontal platforms can’t build a telco ontology. It’s not their business model—they sell the same platform to every industry and expect you to customize through prompts. Building deep telco semantics would mean building healthcare semantics, finance semantics, retail semantics. That’s not their game.

BSS Magic: telco logic at scale

That’s exactly what we built with BSS Magic at Totogi. It’s not another BSS. It’s a telco ontology that imposes domain logic on AI:

• Subscriber truth: person ↔ SIM ↔ line ↔ IMSI
• Product-to-charge relationships: how usage triggers rating, how network elements provision, how legal rules apply
• Instant propagation: change your catalog once; the semantic model updates everywhere, including across all your agents

With BSS Magic, agents don’t drift. They don’t fight. They don’t need re-teaching. Update the ontology once and everything aligns automatically—dramatically safer and cheaper than re-teaching dozens of agents. Ontology centralizes the business logic that’s currently scattered across systems, teams, spreadsheets, ticket flows, human judgment, and tribal knowledge.

It’s the only way agentic AI scales beyond prototypes.

Don’t put the cart before the horse

You have two paths:

Path A: Deploy horizontal agents now. Ask LLMs to “learn” telco through prompts. Brace for semantic drift, execution errors, and nonstop re-teaching. When it collapses, explain to your board why your AI investment became a cautionary tale your competitors reference in their planning meetings.

Path B: Build on ontology first. Impose telco logic on the LLMs. Deploy agents that stay aligned, scale cleanly, and get more valuable over time.

The agent carnage is coming. Horizontal platforms will sell you horizontal solutions. But Totogi already built a telco ontology that’s ready to power whatever AI applications you want to build.

So, be smart and skip the agentic carnage: Start with ontology. Start with Totogi.

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