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Public story · 2026-03-17 · source-backed
arXiv 2603.15566 proposes treating commit history as machine-readable knowledge substrate for coding agents. As AI-written commits lack semantic trails, institutional knowledge loss compounds run-over-run. Directly actionable: structured commits become persistent agent memory across sessions. Source
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ArXiv paper 2603.14805 argues the primary bottleneck in scaling agentic development is knowledge architecture — "skills" (composable, governance-aware units encoding institutional knowledge) are the right primitive for agents, not raw docs or in-context retrieval. Directly rel...
Three-component infrastructure for maintaining agent coherence in a 108K-line C# codebase: hot-memory constitution, 19 specialized domain-expert agents, and cold-memory knowledge base. Quantitative metrics from 283 sessions. Directly actionable for multi-agent coding workflows...
Counterintuitive finding: 36.4% of multi-turn prompts are fully self-contained. Removing prior assistant responses yields up to 10x context length reduction with minimal quality loss. Models over-condition on previous responses, introducing errors. Directly actionable for mult...
This paper tackles the core problem of training command-line agents: long horizons with sparse, delayed rewards when the agent can only partially observe filesystem state. Directly applicable to building autonomous coding assistants and DevOps agents.
Addresses the fundamental privacy dilemma: cloud models need data access but enterprises can't share sensitive information. Splits execution between enterprise-side privacy agents and cloud-side capability agents. Directly relevant to AWS AgentCore and enterprise adoption. arX...
Introduces temporal causal diagnostics to distinguish legitimate task execution from injected manipulation in multi-turn agent interactions, plus context purification to neutralize poisoned content. Directly applicable to anyone building agents that call external tools. arXiv...
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