TeckNexus’s earlier analysis of telecom AI token economics established why falling per-token prices mask rising total inference spend, driven by retrieval overhead and agentic loop depth. This piece extends that analysis specifically to AI agents, where two additional cost drivers, model tier choice and escalation design, sit alongside token volume as the levers that actually determine an agent deployment’s real economics.
| Cost Lever | What It Is | How to Optimise It |
| Model tier choice | Which model handles a given task, frontier vs smaller/faster | Tiered routing: send routine volume to a cheaper model, reserve frontier capability for what needs it |
| Retrieval design | How much context is pulled into the model’s input per interaction | Tune retrieval for precision rather than breadth |
| Escalation cost | The handling cost when an agent defers a task to a human | Track escalation rate and its handling cost as an explicit line item |
Model Choice: The Cost Multiplier Hiding in Plain Sight
Not every task an agent performs needs the same model tier, and routing every interaction through a frontier-capability model by default, rather than matching model tier to task complexity, is one of the most direct and avoidable cost inflators in agent economics. A frontier model capable of complex, multi-step reasoning typically costs meaningfully more per token than a smaller, faster model tuned for narrower, well-defined tasks, and a large share of what a telecom AI agent actually does, routine classification, simple lookups, template-based responses, doesn’t require frontier-level reasoning capability at all. The agents with the most favourable cost economics at scale are consistently the ones built with genuine tiered routing, sending the bulk of routine volume to a smaller, cheaper model and reserving frontier-tier capability specifically for the smaller share of interactions that actually need it, rather than treating model selection as a one-time architectural decision made once and left unexamined as usage scales.
Retrieval Design: Where Cost Hides Inside a Single Interaction
Within a single agent interaction, how much of the token spend goes to retrieving relevant context, pulling documents, prior conversation history, or business data into the model’s input, versus the core reasoning and output generation, is a design choice with direct cost consequences that’s frequently made implicitly rather than deliberately. A retrieval approach that pulls broad, loosely filtered context on every query is simpler to build but meaningfully more expensive to run than one that’s been tuned to retrieve narrowly and precisely for the specific task at hand. This is also, notably, a reliability lever as much as a cost lever: broader, less precise retrieval tends to correlate with a higher rate of the agent constructing a confident answer from loosely relevant context rather than genuinely applicable information, which means the cheaper, more precisely tuned retrieval approach is often also the more reliable one, rather than the two goals trading off against each other.
Escalation Costs: The Line Item Most Cost Models Omit
An agent that escalates a task to a human, rather than completing it autonomously, doesn’t just forgo the labour savings the agent was meant to deliver, it typically adds a real, measurable handling cost of its own: the human reviewer’s time to understand the context the agent has assembled, make the actual decision, and often re-enter or confirm information the agent already processed. A cost model that only accounts for the agent’s own token spend, without accounting for the downstream cost of whatever share of interactions get escalated, will consistently understate the deployment’s true cost, particularly for agent implementations still early in their maturity curve where escalation rates tend to be highest. Tracking escalation rate and its associated handling cost as an explicit line item, alongside direct token spend, gives a far more accurate picture of an agent deployment’s real economics than token cost alone.
Building a Realistic Cost Model
A cost model that accounts for all three levers, tiered model routing rather than flat frontier-model usage, retrieval design tuned for precision rather than breadth, and escalation rate with its associated handling cost, produces a materially more accurate forecast than one anchored purely to a per-token or per-interaction price quoted by a vendor. For an operator evaluating a new AI agent deployment or auditing an existing one, the practical exercise worth running is separating actual spend into these three categories explicitly, model tier cost, retrieval cost, and escalation handling cost, since that breakdown reveals which lever offers the most room for optimisation far more clearly than a single blended cost-per-interaction figure ever will.
How Costs Typically Shift Across an Agent’s Maturity Curve
The relative weight of these three cost drivers doesn’t stay fixed over an agent deployment’s life. Early in deployment, escalation costs tend to dominate, since the agent hasn’t yet accumulated enough validated experience with the operator’s specific edge cases to handle them autonomously, and a conservative, safety-first configuration deliberately routes more borderline decisions to a human than a more mature deployment eventually will. As the agent matures, and as the organisation gains confidence to expand its autonomous scope based on demonstrated reliability, escalation costs typically decline while token and retrieval costs may rise, since a wider range of interactions are now being handled directly rather than deferred to a human. Recognising this shifting pattern matters for budgeting: an operator that models AI agent costs using only the early-deployment cost mix risks under-forecasting the compute-side cost of a mature deployment, while one that assumes mature-deployment cost ratios from day one risks under-forecasting the escalation-handling cost of an agent still building its track record.
Comparing Vendor Cost Claims on a Fair Basis
Vendor-quoted cost figures for AI agent platforms are frequently presented as a simple per-interaction or per-seat price that obscures which of the three cost drivers above is actually being measured, and a fair comparison across vendors requires asking each one to break down its pricing across the same three categories rather than comparing headline figures that may be quietly built on very different assumptions about model tier usage, retrieval scope, or expected escalation rate. A vendor quoting a low headline price built on an assumption of minimal escalation, for instance, may prove considerably more expensive in practice for an operator whose actual use case generates a higher escalation rate than the vendor’s pricing assumed, which is exactly the kind of gap a structured, three-category cost comparison surfaces before a contract is signed rather than after.
Curious where your company stands in the broader telecom AI agent ecosystem? Benchmark your company’s AI agent position against network equipment vendors, OSS/BSS providers, system integrators, and operators.
