Six frameworks for understanding the economics, sovereignty, and governance of enterprise AI agent deployments. Developed by Tony Wenzel, Co-Founder & CEO of Excipio, and published in The Enterprise Token Economy on Substack.
Excipio is a private memory layer that sits between enterprise AI agents and the large language models they call. It intercepts each agent query at a drop-in gateway, serves semantically similar answers from a local cache, and passes genuine cache misses through to your configured model. The result is a modeled 42% reduction in LLM API spend from day one, sub-10ms responses on cache hits, and a customer-owned knowledge graph that compounds with every interaction, with no changes to agent code.
Every LLM-bound agent query is captured at the gateway before it reaches a frontier model. The query is converted to a vector embedding locally, inside your perimeter, and matched against a per-agent index. A semantic match is served in under 10ms; a miss passes through to your configured model. Every resolved query is added to a private, enterprise-owned knowledge graph. It is the same carrier-class intercept-cache-route architecture the founding team has shipped for more than 20 years, built in C++ for the AI agent token economy.
Excipio is a drop-in proxy. You change one BASE_URL environment variable so agent traffic points at Excipio instead of directly at the model API. There is no rearchitecting and no change to agent code, so most deployments are live in hours rather than weeks.
Excipio is model-agnostic and cloud-agnostic by design. Cache misses can route to AWS Bedrock, OpenAI, Anthropic, Azure or Vertex, or a local open-weight model, and you can run on AWS, Azure, GCP, or on-premise, at the same time. You can switch or add models without touching agent code.
Excipio combines an annual platform commitment with a consumption component tied to the savings we measure, so cost scales with the value delivered. Contact us for specifics.
Prompt caching reduces the cost of re-sending context, but your intelligence still flows out to the vendor's model. A routing gateway switches traffic between models. Excipio does both caching and routing, but its defining difference is sovereignty: queries are resolved against a knowledge graph the enterprise owns, so validated answers compound inside your perimeter instead of inside someone else's model.
On a cache hit the query is resolved locally and zero bytes leave your perimeter, so agent query intent and proprietary logic are not transmitted to an external model on those calls. Per-agent sensitivity tiering lets each agent carry its own data-classification and routing policy. Excipio is designed to support GDPR, HIPAA, and SOC 2 at the architecture level, helping preserve confidentiality as a property of the design rather than a contractual promise.
A cache hit is when an incoming agent query is semantically similar to one Excipio has already answered, so the stored answer is served instead of calling a frontier model. Hits return in under 10ms at a fraction of the cost of a fresh call. On day one, around 42% of agent queries are served from cache, the cache hit rate, and that share compounds as the knowledge graph grows. Together with passing the remaining misses through to your configured model, this is what produces the modeled 42% reduction in LLM API spend.
Every validated cache hit adds a query-answer pair to a private knowledge graph that belongs to the enterprise, not to Excipio and not to any model provider. It grows more accurate and more valuable with every agent interaction, turning traffic that would otherwise leak intelligence to a frontier model into a compounding, owned asset. This is what firm sovereignty means: controlling where your institutional intelligence compounds.
Excipio is built for enterprises running AI agents at scale, especially regulated industries such as banking, insurance, and healthcare where cost, latency, model portability, and data governance all matter at once. It speaks to four buyers at once: the FinOps and finance owners focused on token spend, the CTO on performance and portability, the CISO and General Counsel on data protection, and the Chief Data Officer and board on owning institutional intelligence.
The cost enterprises pay when AI agents re-process answers they already have, billing at full frontier rates for queries already resolved.
Read the full definition →The institutional intelligence enterprises transmit to external LLM providers on every agent query that exits the perimeter.
Read the full definition →The ratio of necessary spend to total spend in an enterprise AI agent deployment. Necessary spend divided by total spend.
Read the full definition →The strategic property of an enterprise controlling where its institutional intelligence compounds. Named by Satya Nadella at Davos, January 2026.
Read the full definition →The principle that AI compliance constraints should be enforced at the infrastructure layer, not through policy documents or contractual promises.
Read the full definition →AI agent memory lets agents reuse what they have learned. Sovereign memory architecture is the enterprise form that keeps governed memory inside the perimeter, invariant by design.
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