The Challenge of Redundancy in Agentic Workflows

Agentic systems that utilize plan-execute architectures often suffer from significant latency and high computational costs due to repeated execution of similar sub-tasks. In complex workflows, agents frequently re-generate plans or execute identical tool calls for semantically overlapping user queries. The authors argue that standard caching mechanisms are insufficient because they rely on exact string matches, failing to capture the nuance of intent or the temporal decay of information relevance in dynamic environments.

Temporal Semantic Caching as a Solution

To address these inefficiencies, the paper proposes a 'Temporal Semantic Caching' (TSC) mechanism. Unlike traditional caches, TSC evaluates the similarity of incoming requests against a vector database of previous execution results. By incorporating a temporal decay factor, the system ensures that cached results remain relevant to the current state of the environment.

Key components of this approach include:

  • Semantic Embedding: Using vector representations to identify when a new task is functionally equivalent to a previously executed one.
  • Temporal Weighting: Applying a decay function to cached entries, ensuring that older, potentially stale data is prioritized lower than recent, high-confidence results.
  • Workflow Pruning: Integrating the cache directly into the plan-execute loop, allowing the agent to 'short-circuit' the execution phase if a semantically similar result is found in the cache, thereby bypassing costly LLM inference cycles.

Performance and Trade-offs

The authors demonstrate that this approach significantly reduces the average time-to-completion for multi-step agentic tasks. By optimizing the workflow, the system achieves a balance between accuracy and speed. However, the paper notes a critical trade-off: the overhead of performing vector similarity searches and managing the temporal cache must be lower than the cost of the LLM calls being avoided. The effectiveness of the system is highly dependent on the threshold settings for semantic similarity; setting these too high leads to false positives (incorrectly reusing stale data), while setting them too low negates the performance benefits of the cache.