PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

MVP Investment

$9K - $12K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

R

Rahul Nanda

Meta Platforms, Inc.

C

Chandra Maddila

Meta Platforms, Inc.

S

Smriti Jha

Meta Platforms, Inc.

E

Euna Mehnaz Khan

Meta Platforms, Inc.

Find Similar Experts

AI experts on LinkedIn & GitHub

References (22)

[1]
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
2026Shraddha Barke, Arnav Goyal et al.
[2]
Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub
2026Ramtin Ehsani, Sakshi Pathak et al.
[3]
VIGIL: A Reflective Runtime for Self-Healing Agents
2025Christopher Cruz
[4]
AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress
2025Zhiheng Xi, Chenyang Liao et al.
[5]
Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectories
2025Oorja Majgaonkar, Zhiwei Fei et al.
[6]
Where LLM Agents Fail and How They can Learn From Failures
2025Kunlun Zhu, Zijia Liu et al.
[7]
When Agents go Astray: Course-Correcting SWE Agents with PRMs
2025Shubham Gandhi, Jason Tsay et al.
[8]
Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories
2025Islem Bouzenia, Michael Pradel
[9]
TRAIL: Trace Reasoning and Agentic Issue Localization
2025Darshan Deshpande, Varun Gangal et al.
[10]
AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents
2025Haoyu Wang, Christopher M. Poskitt et al.
[11]
Why Do Multi-Agent LLM Systems Fail?
2025Mert Cemri, Melissa Z. Pan et al.
[12]
Process Reward Models for LLM Agents: Practical Framework and Directions
2025Sanjiban Choudhury
[13]
ARM: Autonomous Remediation & Management with LLM Agents for Intent-Driven Control
2025Vasilis Avgerinos, Kostas Ramantas et al.
[14]
A Survey on LLM-as-a-Judge
2024Jiawei Gu, Xuhui Jiang et al.
[15]
OpenHands: An Open Platform for AI Software Developers as Generalist Agents
2024Xingyao Wang, Boxuan Li et al.
[16]
RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
2024Islem Bouzenia, Prem Devanbu et al.
[17]
A Survey on Large Language Models for Software Engineering
2023Quanjun Zhang, Chunrong Fang et al.
[18]
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
2023Lianmin Zheng, Wei-Lin Chiang et al.
[19]
Large Language Models for Software Engineering: Survey and Open Problems
2023Angela Fan, Beliz Gokkaya et al.
[20]
ReAct: Synergizing Reasoning and Acting in Language Models
2022Shunyu Yao, Jeffrey Zhao et al.

Showing 20 of 22 references

Founder's Pitch

"Automated recovery system for coding agent misbehaviors to enhance software development efficiency."

AI Development ToolsScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

3/4 signals

7.5

Series A Potential

4/4 signals

10

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/19/2026

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

Without systems like Wink, coding agents could frequently deviate from intended tasks, resulting in inefficient software development processes and increased manual interventions.

Product Angle

To productize Wink, it could be offered as a subscription-based plugin for development environments or as an API service for enterprises managing large development teams.

Disruption

Wink can replace manual intervention processes currently employed in software development cycles, streamlining error management in autonomous coding aids.

Product Opportunity

The market size encompasses millions of software developers and enterprises using autonomous coding tools, who would benefit from reduced development time and increased accuracy.

Use Case Idea

Integrate Wink into popular IDEs like IntelliJ or Visual Studio Code to automate the recovery of coding agents, reducing the need for developer intervention and increasing productivity.

Science

Wink is a system that monitors coding agents in real-time, identifies deviations from expected behaviors, and uses automated feedback to correct course and ensure task completion as intended.

Method & Eval

The paper describes testing Wink on 10,000 real-world agent trajectories, achieving a 90% success rate in resolving issues requiring a single intervention, with further improvements demonstrated in live production tests.

Caveats

The system’s efficacy is limited by the accuracy of its classifiers; incorrect course corrections could also lead to further errors if not properly validated externally.

Author Intelligence

Rahul Nanda

Meta Platforms, Inc.
rahulnanda@meta.com

Chandra Maddila

Meta Platforms, Inc.
cmaddila@meta.com

Smriti Jha

Meta Platforms, Inc.
smrj@meta.com

Euna Mehnaz Khan

Meta Platforms, Inc.
eunakhan@meta.com

Matteo Paltenghi

Meta Platforms, Inc.
matepalte@meta.com

Satish Chandra

Meta Platforms, Inc.
satch@meta.com