Spec Kit Agents: Context-Grounded Agentic Workflows
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain "context blind" in large, evolving repositories, leading to hallucinated APIs and architectural violations. We present Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-level, context-grounding hooks. Read-only probing hooks ground each stage (Specify, Plan, Tasks, Implement) in repository evidence, while validation hooks check intermediate artifacts against the environment. We evaluate 128 runs covering 32 features across five repositories. Context-grounding hooks improve judged quality by +0.15 on a 1-5 composite LLM-as-judge score (+3.0 percent of the full score; Wilcoxon signed-rank, p < 0.05) while maintaining 99.7-100 percent repository-level test compatibility. We further evaluate the framework on SWE-bench Lite, where augmentation hooks improve baseline by 1.7 percent, achieving 58.2 percent Pass@1.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 34 references that are in the map. First posted on arXiv 7 Apr 2026.
Builds on
17 of 34 references.
Closest ancestors · 4
Same direction, newest first
- 2025ReVeal: Self-Evolving Code Agents via Reliable Self-Verification13 Jun
- 2025DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal18 Mar
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024Agentless: Demystifying LLM-based Software Engineering Agents1 Jul
Other references · 13
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- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
- 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
- 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
- 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
- 2023Teaching Large Language Models to Self-Debugcited by 988
- 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
- 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591
- 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 339
- 2022MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoningcited by 156
1 more
- 2025Multi-Agent Collaboration via Evolving Orchestrationcited by 108
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Citing papers per year: 2026: 1.
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