Oct 29, 2026 · 12 min
Open-source tools target AI agents’ rising token costs
Cut Token Costs: Faster AI Agents with Slancha AI
As agent behavior becomes more capable but less predictable, Slancha’s approach links skill evaluation, workload routing, and secure connections to more controlled AI operations.
- 1Skill replay and evaluation can make agent behavior more measurable while exposing regressions and security risks.
- 2Mesh routes workloads among specialized models and distributed GPUs instead of sharding one model across machines.
- 3Wire creates an encrypted path for connecting agents with external systems and demanding services across networks.
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Paul Logan connects skill replay, distributed model routing, and encrypted agent communication into one efficiency strategy.
The brief
Paul Logan, Slancha’s founder and a former NVIDIA innovator, argues that rising token costs make agent efficiency urgent—and stochastic behavior makes optimization difficult.
InGit, Slancha’s open-source skill control plane, lets teams publish skills, define evaluations, track changes, and see whether revisions improve, degrade, or introduce security risks.
Slancha Mesh connects specialized models on different machines behind one API, routing requests across distributed GPUs rather than sharding a single model.
Wire adds an encrypted channel between agents, networks, and external services, opening a path from local Hermes workflows to demanding orchestration through services such as Claude.
The episode’s central proposition is practical: replay and evaluate proven skills, route each task to the right model, and secure the connections around them.
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