Sold by: Cognizant
Cognizant® Agent Economics is a FinOps framework purpose-built for agentic AI that attributes LLM cost per task — not per token — across teams, agents, and environments in real time.
It combines expert-led services, eval-gated engineering optimization, and a continuous Monitor → Optimize → Improve operating model to help enterprises manage LLM spend as a strategic investment. Deployed on one gateway with open standards and zero source changes, the framework delivers real-time spend visibility, six eval-gated optimization rules (semantic caching, tier detection, model routing, prompt compression, context management, batch detection), runaway-cost detection, and chargeback attribution — typically recovering 50–70% of LLM waste while holding quality to an eval gate. Available as a phased engagement starting with a low-risk, fixed-fee readiness assessment.
Overview
Enterprise AI spend has outpaced financial governance. Cloud FinOps tools optimize the resource-hour — instances, storage, egress — but agentic AI is billed by the resolved task, not the VM. A model downgrade needs an eval gate, not just a price comparison. Rate optimization was a procurement problem; agent optimization is an engineering problem with a quality constraint. According to Gartner, agentic models can multiply token consumption 5 to 30 times per task compared with a standard GenAI chatbot, and costs accumulate quickly within a single session.
Cognizant® Agent Economics closes this gap. It is a FinOps framework purpose-built for agentic AI that maps the proven FinOps lifecycle — Inform, Optimize, Operate — onto the LLM stack through a continuous Monitor → Optimize → Improve operating model, delivered on one LLM gateway with open standards.
MONITOR: Gateway-metered telemetry, a six-tag attribution schema (team, environment, agent, feature, user segment, use case), cost-per-task analytics, a chargeback engine, and runaway detection provide full spend visibility in real time.
OPTIMIZE: Six eval-gated rules run continuously — semantic caching, tier detection, model routing, prompt compression, context management, and batch detection. Each is replayed against real production traffic through an eval gate before rollout, so costs fall without silent quality degradation.
IMPROVE: Drift counter-measures lock in gains — CI/CD prompt-hygiene gates, quarterly model re-benchmarks, chargeback ownership, and gateway guardrails as policy-as-code keep savings compounding as models, prompts, and usage evolve.
The platform is built on open-source foundations and supports AWS services, with the flexibility of being model- and cloud-agnostic. Running agents are auto-discovered from traces with zero source changes — the SDK is optional.
Delivered as four phased services — workshop, readiness assessment, control-plane build and optimization engineering and managed operation — Agent Economics offers a low-risk entry that pays for itself in identified savings.
Highlights
- Eval-Gated Optimization, Not Advisory: Every cost-reduction change — model tiering, prompt compression, routing — replays real production traffic through an eval gate before rollout, so costs fall without silent quality degradation. Economics are computed per resolved task and per agent, giving finance the unit it needs to budget and charge back. Organizations without LLM FinOps typically overspend 50–70% versus optimized peers.
- Deploys as a single gateway in the agent traffic path with optional SDK. Running agents are auto-discovered from Open Telemetry traces with zero source changes
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For inquiries, engagement scoping, and support, contact AIMUDeals@cognizant.com Cognizant provides dedicated support throughout all three engagement stages — Understand and Reimagine, Blueprint and Build, and Deploy and Measure — with named AI Customer Engineers assigned to each engagement.