Built a C# AI Agent That Researches Errors and Suggests Fixes
A developer has created a C# AI agent designed to assist with debugging by researching errors and suggesting fixes. This AI agent, named DotNetErrorAgent, utilizes external sources such as GitHub issues and StackOverflow discussions to provide evidence-based solutions. The goal is to mimic the process of a senior engineer, ensuring that the AI does not rely solely on internal knowledge.
- ▪The AI agent is built using C# and integrates with Semantic Kernel and Azure OpenAI.
- ▪It is designed to research external sources before providing answers to debugging questions.
- ▪The agent follows a structured analysis process to identify and rank potential causes of errors.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/kaushal_kumar_4c40dae4fcc/built-a-c-ai-agent-that-researches-errors-and-suggests-fixes-pj6 |
| Publication time | Tue, 26 May 2026 17:57:12 +0000 |
| Retrieval time | 2026-05-26T18:07:50.919Z |
| Last seen | 2026-05-26T18:07:50.919Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | KD8nAMKmWnPf |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3952994) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Kaushal Kumar Posted on May 26 Built a C# AI Agent That Researches Errors and Suggests Fixes #dotnet #csharp #ai #semantickernal We've all done this. You hit an exception You copy the stack trace Open Google Read StackOverflow Open GitHub issues Check Microsoft docs Read random blogs Open three more tabs Thirty minutes later you're still debugging. Modern AI tools help, but there is a problem: They often answer from memory.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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