LLM Proxy
An intermediary service positioned between AI agents and LLM providers that routes requests to different models or providers based on request content (such as keywords) or the current speed of each channel, in order to optimize cost and latency.
Timeline (last 90 days)
Schemas differ enormously across data sources (date formats, currency units, missing-value representations, and nesting levels all vary), and after receiving raw JSON, the agent often needs an extra LLM step for field mapping, which both increases token costs and introduces uncertainty.
An LLM proxy is an intermediary layer between the application and the LLM provider that records requests and responses to monitor latency, cost, errors and model behavior.
In LLM agent scenarios, least privilege must be refined into "per-call least privilege," because if the permission boundary is tied to a session rather than to a single call, it keeps expanding as the chain lengthens.
The author's LLM agent runs on localhost, exposes OpenAI- and Anthropic-compatible endpoints, uses Aho-Corasick string matching for keyword routing with microsecond-level overhead, and shares the same crate as the knowledge graph.
The author emphasizes that the saving pattern is routing per session or per scenario, rather than randomly switching models every turn; one should pick a lane for a task and stay on it.
Under the shared conversation memory condition, the LLM agent deviated from the current customer's goal in 22% of eligible decisions, being interfered with by goal representations left over from previous interactions.
Persistent LLM agents lack relationship-specific state isolation, and goal representations in the KV cache leak across relationship boundaries and affect subsequent decisions.
Although shared memory improves context coherence, it may come at the cost of decision faithfulness.
Maintaining an independent cache partition for each customer relationship, actively clearing or masking irrelevant goal representations when switching customers, or designing an attention mechanism that dynamically marks goal attribution, are potential engineering directions for dealing with goal leakage.
When designing multi-customer agent systems, state isolation should not be regarded as an optional optimization item, but as a foundational requirement for ensuring behavioral faithfulness.
All Facts (10)
Schemas differ enormously across data sources (date formats, currency units, missing-value representations, and nesting levels all vary), and after receiving raw JSON, the agent often needs an extra LLM step for field mapping, which both increases token costs and introduces uncertainty.
50%UnverifiedAn LLM proxy is an intermediary layer between the application and the LLM provider that records requests and responses to monitor latency, cost, errors and model behavior.
50%UnverifiedIn LLM agent scenarios, least privilege must be refined into "per-call least privilege," because if the permission boundary is tied to a session rather than to a single call, it keeps expanding as the chain lengthens.
50%UnverifiedThe author's LLM agent runs on localhost, exposes OpenAI- and Anthropic-compatible endpoints, uses Aho-Corasick string matching for keyword routing with microsecond-level overhead, and shares the same crate as the knowledge graph.
50%UnverifiedThe author emphasizes that the saving pattern is routing per session or per scenario, rather than randomly switching models every turn; one should pick a lane for a task and stay on it.
50%UnverifiedUnder the shared conversation memory condition, the LLM agent deviated from the current customer's goal in 22% of eligible decisions, being interfered with by goal representations left over from previous interactions.
50%UnverifiedPersistent LLM agents lack relationship-specific state isolation, and goal representations in the KV cache leak across relationship boundaries and affect subsequent decisions.
50%UnverifiedAlthough shared memory improves context coherence, it may come at the cost of decision faithfulness.
50%UnverifiedWhen designing multi-customer agent systems, state isolation should not be regarded as an optional optimization item, but as a foundational requirement for ensuring behavioral faithfulness.
50%UnverifiedMaintaining an independent cache partition for each customer relationship, actively clearing or masking irrelevant goal representations when switching customers, or designing an attention mechanism that dynamically marks goal attribution, are potential engineering directions for dealing with goal leakage.
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