Three-part architecture tackles context bloat in Anthropic agent loops
u/Able-Chapter-5820 shares a three-component pattern using Claude 3 Opus and 3.5 Sonnet to manage context window bloat and API latency in continuous multi-agent loops.
Score breakdown
The pattern directly addresses two concrete costs of long-running agent loops — context window exhaustion and API latency spikes — by combining caching, lazy schema loading, and model-role separation with an intermediate compaction step.
- 01Author u/Able-Chapter-5820 describes the pattern as working well for managing memory and compute in continuous agentic loops.
- 02KV prompt caching isolates latency by keeping core instructions and static context cached rather than resending them each turn.
- 03Tool schemas are loaded dynamically based on the agent's initial routing decision, rather than stuffed into the initial context.
u/Able-Chapter-5820 describes a three-component architecture developed through hands-on deployment of multi-agent systems, aimed at preventing context window bloat and the API latency spikes that accompany it in continuous agentic loops using Anthropic's Claude models.
The third and most architecturally distinctive component is the "Advisor Strategy," which decouples the execution and advisory roles across two models.
The first component is KV prompt caching: rather than resending the full system prompt on every loop turn, static instructions and context are kept in a KV cache, which the post credits with significantly speeding up loop iteration. The second component is deferred tool schema loading — instead of front-loading every possible tool schema into the initial context, schemas are loaded dynamically only when the agent's initial routing step determines they may be needed, directly addressing a primary source of context bloat.
The third and most architecturally distinctive component is the "Advisor Strategy," which decouples the execution and advisory roles across two models. Claude 3.5 Sonnet serves as the high-speed "Executor" for standard routing and tool calling, while Claude 3 Opus is reserved as the "Advisor" for complex reasoning or error debugging scenarios. Before escalating to Opus, the context passes through a memory compaction and summarization step to keep the handoff lean. After Opus provides its advisory output, control returns to Sonnet. The post closes by inviting discussion on memory compaction approaches such as summarize-and-replace.
Key facts
- 01Author u/Able-Chapter-5820 describes the pattern as working well for managing memory and compute in continuous agentic loops.
- 02KV prompt caching isolates latency by keeping core instructions and static context cached rather than resending them each turn.
- 03Tool schemas are loaded dynamically based on the agent's initial routing decision, rather than stuffed into the initial context.
- 04The 'Advisor Strategy' decouples execution and advisory roles across two models: Claude 3.5 Sonnet as 'Executor' and Claude 3 Opus as 'Advisor'.
- 05Context is passed through a memory compaction/summarization step before being routed to Opus.
- 06After Opus provides advisory output, control is handed back to Sonnet.
Topics
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