This paper investigates how different components of coding harnesses, such as planning, action space, and context management, impact the performance of autonomous coding agents in software engineering tasks. Practitioners might care about understanding how to design harnesses that effectively utilize these components to improve agent performance.
Firehose
Filtered to tagged “harness design” · clear filters
Browse: People · Companies · Papers · Podcasts · Hacker News · Deep dives
Browse by tag
artificial intelligence 82continual learning 29AI 26agentic coding 17reinforcement learning 16open-weight models 11AI agents 10AI safety 9AI ethics 8cybersecurity 8machine learning 7open-source 7language models 6natural language processing 6Reinforcement learning 6artificial general intelligence 5deep learning 5Diffusion models 5Agentic AI 4computer vision 4conversational AI 4ethics 4existential risk 4large language models 4LLMs 4Recursive self-improvement 4robotics 4security 4vision-language models 4ai 3
Andrew Lee, CEO of Tasklet, details his company's complete rewrite of their agent stack, now emphasizing file system context, agentic search, and multi-resolution summarization for token efficiency. He discusses the strategic challenge of c…
Agent architectureFile system contextAgentic searchMulti-resolution summarizationToken conservationLLM pricingHorizontal platformsAPI-first companiesSolutions companiesWorkflow automationGeneral purpose agentsContext managementCaching strategiesAnthropic modelsOpenAI modelsModel capabilitiesComputer useCode generationSupply chain riskModel differentiationHarness designMecha suit conceptOrganizational contextShared brain AIGenerative UIAI safetyData durabilityHuman-in-the-loop AIVendor selectionAI music generationToken economicsAlgorithmic breakthroughsBusiness productivity