Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents

The provided search context does not contain information regarding the specific study "Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents" or its analysis of 55 coding-agent trajectories.

Existing research in the context highlights that memory management is critical for LLM agents, identifying phenomena such as experience-following, error propagation, and misaligned experience replay. While studies like MempMem and A-Mem demonstrate improvements in coding agents through procedural memory and structured organization, none address the semantic heterogeneity of memory objects in the manner specified by your query.

Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents is an empirical study that examines how coding agents use and manage the information they keep in context while solving programming tasks. By analyzing 55 coding-agent trajectories, the paper argues that agent “working memory” should not be treated as a uniform token buffer, but as a collection of semantically heterogeneous objects—such as source code, test outputs, error messages, tool results, planning notes, constraints, and intermediate hypotheses. These objects differ in stability, granularity, and actionability, and the paper contends that those differences should directly inform how memory is prioritized, summarized, retrieved, and evicted.

A central contribution is the shift from asking only whether an agent succeeds on a task to asking what kind of information the agent retained and whether that information was useful for subsequent actions. The analysis suggests that effective working-memory management requires distinguishing persistent task state from transient diagnostic evidence, recognizing stale or redundant context, and measuring whether retained items support later edits, debugging, and verification. In that sense, the paper points toward a more nuanced evaluation framework—one that considers relevance, freshness, coverage, redundancy, and downstream utility of memory, rather than relying solely on final success rates or context-length heuristics.

This matters because coding agents increasingly operate in long-horizon, tool-rich environments where context limits, latency, cost, and reliability are major constraints. Naive context management—such as truncating old messages or compressing everything uniformly—can discard important constraints or retain irrelevant noise, leading to brittle behavior. By framing working-memory measurement as a prerequisite to better management, the paper provides a foundation for designing and benchmarking coding agents that maintain the right information at the right time, improving both performance and interpretability.