Ending Runaway Token Costs in Enterprise AI
Why trusted knowledge and knowledge graphs, not bigger models, are what make agentic automation of customer and business operations economically sustainable.
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What the paper covers
A cost problem that software engineering teams already lived through is now surfacing in customer operations. The mechanism is the same, and so is the fix.
- Why uncontrolled token consumption is almost never a sign of an underpowered model
- The three compounding costs of brute-force retrieval: volume, latency, and reliability
- How to split enterprise knowledge into what should run as deterministic logic and what genuinely needs agentic reasoning
- The five elements of eGain’s Trusted Knowledge architecture, including graph-guided retrieval and agentic state machines
- A side-by-side comparison of uncontrolled and trusted knowledge-guided automation across six dimensions
- Why eGain can underwrite token-cost risk and price engagements on outcomes instead of consumption
One section from inside the paper
Three of the six dimensions compared in full on page five. Below table shows how the two approaches differ in practice.
| Dimension | Uncontrolled agentic automation | Trusted knowledge-guided automation |
|---|---|---|
| Content retrieval | Broad, loosely relevant chunks pulled by similarity search. The model sorts out what matters. | Precise, complete content located by knowledge graph navigation to the exact node needed. |
| Token consumption | Large, unpredictable, and growing with content volume and conversation length. | Small, bounded, and predictable regardless of how much content exists in the enterprise. |
| Commercial risk | The client carries open-ended usage cost risk. | The vendor is confident enough in efficiency to underwrite that risk through outcome-based pricing. |
