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Most telecom and AI coverage right now is hype-driven. It’s stuffed with predictions and vague transformation claims that sound good on stage, but say very little about what actually ships.
The truth about this technology’s potential is actually a lot more exciting. AI is moving from being a tool people use to something they work with. The shift is from consulting AI to embedding it in the work.
“When you use AI, you paste a requirement into a chatbot,” says Dawid Mielnik, Telecom General Manager at Software Mind. “When you work with AI, an agent takes the task from the ticket, reasons over the actual specs and project history, does the mechanical work, and hands it back at a decision point. This all occurs under telecom’s real constraints of standards, protocols, and compliance.”
Here are three production-grade capabilities that show what’s really working today.
1. Protocol-fluent AI for telecom engineering
The hard part of telecom engineering is rarely the coding itself. It is getting a change into production without breaking a standard or an interoperability assumption.
If a product owner wants to support a new standard, engineers begin the work by interpreting the requirement and locating the relevant 3GPP references. Once they map edge cases and update the internal design docs, they can begin coding. All of that prep work can be systematized.
“For example, we built an engineering workflow around the telecom change of adding a new roaming-control option (“all packet-oriented services barred”) based on the 3GPP TS 29.272 standard,” Mielnik says. “The AI agents take tagged issues from GitLab and draft an implementation specification for a human to review. They then implement the change on a new branch and open a pull request. After deployment, other agents validate the result by checking system logs and network traces (PCAP files). We used standard, general-purpose AI models. The protocol knowledge came from giving the agents the right specs and project context, not from custom-training a model.”
A protocol-fluent approach starts at the source. Instead of asking engineers to copy and paste requirements into a chatbot, intelligent agents pick up work directly from tickets. They read through acceptance criteria and discussion threads, scan CI context, and incorporate prior decisions embedded in the project history. The fluency comes from giving the model the right context: the acceptance criteria, the relevant spec clauses, and the project’s own history.
To prevent problems and expensive rework, the agent maps the requirements against telecom specifications and internal architecture. It traces intent-to-spec clauses and expected message flows, and weighs interoperability constraints.
“We get a detailed implementation specification, including the necessary data model changes and interface updates,” says Mielnik. “The plan also layers in test strategy and rollout considerations.”
Then comes execution. While an agent implements the change in code, a second agent runs overnight against the build, validating the signaling capture, reviewing the logs, and running security and dependency checks.
Engineers arrive the next morning to find a clear report of what passed and what failed, so they know exactly what looks risky and what needs their review. Instead of searching for surprises, they invest their time in informed judgment calls.
“None of these removes the expert,” Mielnik says. “It moves expert attention to the checkpoints, rather than spreading it thin across every mechanical step. You use your expertise where you need it and avoid burning out your best people.”
2. Carrier-grade voice framework
Voice is the toughest test for any of this, and the most revealing. Latency and availability aren’t negotiable, and the call is the product. Most voice AI today sits over the top, next to the call. A carrier-grade approach puts it in the path.
A carrier-grade voice framework offers an entirely different architecture.
“Case in point: when we demoed a voice framework inside the voice path, we built it to be SIP-native and integrated with IMS,” Mielnik explains. “We designed it so that operators can plug in or swap speech recognition and language models without rebuilding the voice platform. In a support-call demo, it produced a live transcript with speaker labels and offered real-time translation. It generated a summary afterward and added analysis such as sentiment and security signals.”
“This architecture puts intelligence directly into your call flow,” Mielnik adds. “Picture a routine support call. A consultant phones a customer to walk them through a new offer. As they talk, the framework transcribes the call live and labels each side, so the consultant and the customer appear as separate speakers in the record instead of one run-on block of text. When the call ends, the consultant gets a summary of what was discussed and what to follow up on, without stopping to write it down. The intelligence was part of the call, not a report stitched together somewhere else afterward.”
The carrier-grade framework also offers flexibility. Carriers and vendors can’t afford to bet the entire voice stack on a single provider’s automatic speech recognition or large language model (LLM). A proper framework provides pluggable components that are embedded but allow clients to switch models without rebuilding the entire call path.
And because this intelligence lives inside the voice infrastructure, it naturally follows carrier-grade operational expectations. It delivers the network-level observability and predictable routing behavior that protect call quality, and offers policy enforcement that turns compliance into a built-in feature.
“Your voice applications stop being retrofits and become part of the telecom core,” Mielnik says.
3. Telecom network operations are powered by workflow AI
Consider what happens on a network team every time an incident occurs: alerts fire and dashboards light up; packet captures are pulled, historical tickets are searched, recent configuration changes are reviewed, and someone senior tries to assemble a coherent story from an overwhelming collection of partial signals.
“Moments like these are when we need to move quickly, but all of that tedious work takes time because the information we need is typically scattered across many locations,” says Mielnik. “Failures also rarely spring from a single issue. Tiny errors often combine. The resulting issue looks entirely random until we reconstruct the chain.”
A production-grade approach uses a system with multiple agents to meet that reality. One agent acts as a router. It triages incoming issues by classifying severity and dispatching work to the right specialists.
Specialized agents then work alongside each other. While one parses packet captures and checks behavior against expectations, a second correlates symptoms with recent code changes, and another digs through historical tickets to find evidence of recurrence. These multiple agents working in parallel drastically change the pace.
A verdict agent produces a full root cause analysis in minutes. It suggests recommended fixes and offers a plain-language narrative that all departments will be able to understand. An accompanying confidence score lets engineers know when to verify those results. Incident memory makes the next investigation even faster.
“One multi-agent setup we created helps troubleshoot incidents end-to-end,” Mielnik recalls. “In this example, one agent routes the case, another checks SIP traces against standards like RFC 3261 and 3GPP rules, another searches past code changes and related tickets in Jira/GitLab, and a final agent writes up the likely root cause with quotes from the relevant RFCs, an action recommendation, and a confidence score.”
In Software Mind’s test scenario for this multi-agent setup, Mielnik states that the issue appeared to be a voicemail problem because a SIP re-INVITE was being ignored. However, the system traced the real cause to an earlier step. The initial SIP INVITE was missing the required Contact header (RFC 3261, section 8.1.1.8). It reported that conclusion with 72% confidence.
“When we implement this technology well, this turns incident resolution from hours of evidence-gathering into minutes,” Mielnik explains. “We don’t skip rigor, and we still rely on experts to own the decisions. Those experts just spend a lot less time gathering evidence and can instead put their effort into the fastest path forward.”
Getting the question right
These three examples show how the technology people use today affects various parts of the telecom landscape.
“We are learning to ask less about a model’s intelligence and capabilities and ask more questions about where and how we integrate that model,” Mielnik concludes. “Is it embedded into real workflows and infrastructure? That’s when we’re talking about intelligent agents as working partners inside the systems we operate every day. Get that conversation right, and the AI that looks best in a hyped-up demo gets a lot more difficult to accept.”
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