Spec Kit Agents: Context-Grounded Agentic Workflows
- URL: http://arxiv.org/abs/2604.05278v1
- Date: Tue, 07 Apr 2026 00:26:49 GMT
- Title: Spec Kit Agents: Context-Grounded Agentic Workflows
- Abstract summary: Spec Kit Agents is a multi-agent SDD pipeline with PM and developer roles.<n>Read-only probing hooks ground each stage in repository evidence.<n> validation hooks check intermediate artifacts against the environment.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Grounding AI Agents in Contracts: An Empirical Evaluation of Spec-Driven Test Generation [4.061618191574875]
Spec-Driven Test Generation instructs an agent to first reason about -- and explicitly document -- code pre-conditions, post-conditions, and undefined behaviors.<n>This intermediate semi-formal specification acts as a cognitive scaffold to guide subsequent test generation.
arXiv Detail & Related papers (2026-08-17T22:36:12Z) - Efficient Visual Pointing for Embodied AI:Agent-Driven Data Synthesis, Cross-Block Attention, and Iterative Correction [55.11480729304395]
We describe our PointArena 2026 solution, which achieves 77.2% overall accuracy and ranks second on the benchmark.<n>The ap proach targets three failure modes. First, agent-driven syn thesis builds large semantic and anchor-relative candidate pools.<n>Second, a determinis tic steerable-data pipeline creates a verified 10,000-sample main set, plus reserve samples, using masks, templates, and path verification.
arXiv Detail & Related papers (2026-06-29T06:39:03Z) - A Deterministic Control Plane for LLM Coding Agents [0.0]
A prevalence study of 10,008 GitHub repositories finds that agent configurations propagate as undeclared shared components.<n>We propose a deterministic control plane above the harness that maps one-to-one to these gaps.
arXiv Detail & Related papers (2026-06-25T12:02:18Z) - VisualClaw: A Real-Time, Personalized Agent for the Physical World [76.89211120690028]
We present VisualClaw, a self-evolving multimodal agent built around two principles.<n>First, hybrid encoding reduces deployment cost by filtering less informative streaming frames with a cascaded gate.<n>Second, skill evolution lets the agent learn from failures, producing skill-bank updates that help future questions.
arXiv Detail & Related papers (2026-06-15T06:58:22Z) - Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows [8.909548943361637]
BenchAgent places single-agent, fixed multi-agent (MAS) and evolving MAS under one normalized execution and logging protocol.<n> BenchAgent evaluates these substrate-internal across ten reasoning, coding, and tool-use benchmarks with GPT-4.1.<n>On the PAE GAIA snapshot, a Claude-Code-style runtime workflow reaches 66.72% overall and 69.23% on Level 3.
arXiv Detail & Related papers (2026-06-04T03:50:47Z) - AgentLens: Revealing The Lucky Pass Problem in SWE-Agent Evaluation [11.272830796781925]
We evaluate 2,614 OpenHands trajectories from eight model backends on 60 SWE-bench Verified tasks.<n>Among passing trajectories in this subset, 10.7% exhibit behavior we call a Lucky Pass.<n>We introduce AgentLens, a framework for process-level assessment of SWE-agent trajectories.
arXiv Detail & Related papers (2026-05-13T03:00:57Z) - How Far Is Document Parsing from Solved? PureDocBench: A Source-TraceableBenchmark across Clean, Degraded, and Real-World Settings [56.70440596502351]
The past year has seen over 20 open document parsing models, yet benchmarks almost exclusively on OmniDocBench.<n>We present PureDocBench, a benchmark thatrenders document images from HTML/CSS, covering 10 domains, 66 pages, and 1,475 pages.
arXiv Detail & Related papers (2026-05-08T09:30:31Z) - ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces [17.202580606345666]
Large language model (LLM) agents are increasingly deployed to automate productivity tasks.<n>Existing benchmarks rely on simplified environments and fail to capture realistic, multi-service sandbox.<n>We introduce ClawsBench, a benchmark for evaluating and improving LLM agents in realistic productivity settings.
arXiv Detail & Related papers (2026-04-06T21:09:06Z) - ClawArena: Benchmarking AI Agents in Evolving Information Environments [61.664633997138004]
ClawArena is a benchmark for evaluating AI agents in evolving information environments.<n>Each scenario maintains a complete hidden ground truth while exposing the agent only to noisy, partial, and sometimes contradictory traces.<n> evaluation is organized around three coupled challenges: multi-source conflict reasoning, dynamic belief revision, and implicit personalization.
arXiv Detail & Related papers (2026-04-05T17:55:23Z) - A Benchmark for Evaluating Repository-Level Code Agents with Intermediate Reasoning on Feature Addition Task [11.218318079376365]
RACE-bench is a reasoning-augmented benchmark for evaluating code agents on feature addition tasks.<n> RACE-bench contains 528 real-world feature addition instances from 12 open-source repositories.<n>We evaluate three representative repository-level code agents on RACE-bench.
arXiv Detail & Related papers (2026-03-27T11:58:47Z) - Governing Dynamic Capabilities: Cryptographic Binding and Reproducibility Verification for AI Agent Tool Use [0.0]
No existing security layer verifies what an AI agent can do, whether it executed what it claims, or what happened in a multi-agent interaction.<n>Existing frameworks conflate the two, enabling silent capability escalation and leaving interactions without verifiable provenance.<n>We derive three Agent Governance Requirements: capability integrity (G1), behavioral verifiability (G2), and interaction auditability (G3)<n>We validate with two crypto-agnostic instantiations -- basic (Ed25519, SHA-256; 97 us verify) and enhanced (BBS+ selective disclosure, Groth16 DV-SNARK; 13.8 ms)
arXiv Detail & Related papers (2026-03-15T11:46:57Z) - TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation [19.43198506241428]
We present TestExplora, a benchmark designed to evaluate Large Language Models as proactive testers.<n>TestExplora contains 2,389 tasks from 482 repositories and hides all defect-related signals.<n>Our evaluation reveals a significant capability gap: state-of-the-art models achieve a maximum Fail-to-Pass (F2P) rate of only 16.06%.
arXiv Detail & Related papers (2026-02-11T03:22:51Z) - RepoGenesis: Benchmarking End-to-End Microservice Generation from Readme to Repository [52.98970048197381]
RepoGenesis is the first multilingual benchmark for repository-level end-to-end web microservice generation.<n>It consists of 106 repositories (60 Python, 46 Java) across 18 domains and 11 frameworks, with 1,258 API endpoints and 2,335 test cases verified.<n>Results reveal that despite high AC (up to 73.91%) and DSR (up to 100%), the best-performing system achieves only 23.67% Pass@1 on Python and 21.45% on Java.
arXiv Detail & Related papers (2026-01-20T13:19:20Z) - OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding [57.39403818250357]
We introduce OctoBench, which benchmarks scaffold-aware instruction following in repository-grounded agentic coding.<n> OctoBench includes 34 environments and 217 tasks instantiated under three scaffold types, and is paired with 7,098 objective checklist items.<n>Experiments reveal a systematic gap between task-solving and scaffold-aware compliance, underscoring the need for training and evaluation.
arXiv Detail & Related papers (2026-01-15T12:36:08Z) - SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
arXiv Detail & Related papers (2025-03-11T17:53:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.