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📝 WalkthroughWalkthroughChangesDeveloper Lightspeed AI documentation
Estimated code review effort: 1 (Trivial) | ~5 minutes Sequence Diagram(s)sequenceDiagram
participant IDEExtension
participant SolutionServer
participant Agent
participant LLM
IDEExtension->>SolutionServer: request migration insights
SolutionServer->>Agent: provide resolution context
Agent->>LLM: request code suggestions
LLM-->>Agent: return suggested resolution
Agent-->>IDEExtension: present local changes for review
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Actionable comments posted: 4
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@docs/topics/developer-lightspeed/con_agent-ai.adoc`:
- Around line 12-16: Keep the Agent AI approval boundary explicit: in
docs/topics/developer-lightspeed/con_agent-ai.adoc lines 12-16, describe
generated local suggestions as requiring user review and approval before
modifications are applied; in
docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc
line 23, clarify that agent automation does not remove this review step.
In
`@docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc`:
- Line 13: Update the Solution Server description to state only the documented
behavior: accepted resolutions generate solved examples and migration hints that
inform future prompts. Remove or qualify the claim that rejected code changes
are evaluated, ingested, or stored unless that behavior is explicitly documented
elsewhere.
- Line 13: Update the Solution Server flow description and the corresponding
references at the other indicated occurrences to remove direct-to-LLM wording.
State that requests are proxied through the LLM proxy and credentials or secrets
remain managed within the cluster and are consumed server-side; keep the
surrounding integration behavior unchanged.
- Line 15: Update the RAG description in the developer Lightspeed integration
documentation to state that RAG retrieves and augments context, while the LLM
uses that context to generate issue resolutions. Rephrase the sentence without
implying that RAG itself produces code fixes.
🪄 Autofix (Beta)
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ℹ️ Review info
⚙️ Run configuration
Configuration used: defaults
Review profile: CHILL
Plan: Pro Plus
Run ID: e5eac0cd-8be7-4167-97a3-fafd59a0b3cc
📒 Files selected for processing (4)
docs/topics/developer-lightspeed/assembly_understanding_dl.adocdocs/topics/developer-lightspeed/con_agent-ai.adocdocs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adocdocs/topics/developer-lightspeed/con_solution_server.adoc
Comment on lines
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| To understand client-side autonomous execution, review how the Agent AI operates within your environment. The Agent AI performs local code modifications directly in your workspace. | ||
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| * Plans the context to define the issues. | ||
| * Chooses a suitable sub agent for the analysis task. {mta-dl-plugin} works with the LLM to generate fix suggestions. The LLM displays the reasoning transcript and files to be changed. | ||
| * Applies the changes to the code once you approve the updates. | ||
| The Agent AI operates within the developer integrated development environment (IDE). It executes targeted fixes on specific files without requiring a full hub re-analysis cycle. | ||
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| If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this iteration, the agentic AI attempts to fix diagnostic issues (if any) generated by tools that you installed in the Visual Studio Code IDE. You can review the changes and accept the agentic AI's suggestion to address these diagnostic issues. | ||
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| After each iteration of applying changes to the code, the agentic AI asks if you want the agent to continue fixing more issues. When you accept, it runs another iteration of automated analysis until it has resolved all issues or it has made a maximum of two attempts to fix an issue. | ||
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| Agentic AI generates a new preview in each iteration when it updates the code with the suggested resolutions. The time taken by the agentic AI to complete all iterations depends on the number of new diagnostic issues that are detected in the code. | ||
| When diagnostic or migration issues are discovered in a file, the Agent AI generates local resolution suggestions. It presents proposed changes directly within the workspace for user review before applying them. |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
Keep the Agent AI approval boundary consistent.
The two descriptions can imply automatic application of fixes, while the documented workflow requires users to review proposed changes before they are applied.
docs/topics/developer-lightspeed/con_agent-ai.adoc#L12-L16: describe local suggestions and user-approved modifications.docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc#L23-L23: clarify that agent automation does not remove the review step.
📍 Affects 2 files
docs/topics/developer-lightspeed/con_agent-ai.adoc#L12-L16(this comment)docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc#L23-L23
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@docs/topics/developer-lightspeed/con_agent-ai.adoc` around lines 12 - 16,
Keep the Agent AI approval boundary explicit: in
docs/topics/developer-lightspeed/con_agent-ai.adoc lines 12-16, describe
generated local suggestions as requiring user review and approval before
modifications are applied; in
docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc
line 23, clarify that agent automation does not remove this review step.
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| == Collaborative workflow | ||
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| The Solution Server is an independent backend component that builds a collective memory of your migration projects. Your editor extension communicates directly with the large language model (LLM) or routes requests through an LLM proxy. The Solution Server evaluates your accepted and rejected code changes to extract additional insights and resolution patterns. It feeds these insights back to the extension to enhance the prompt context for future LLM solution requests. |
Contributor
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🗄️ Data Integrity & Integration | 🟠 Major | ⚡ Quick win
Limit the Solution Server memory claim to documented behavior.
This says both accepted and rejected changes are evaluated, but the supplied behavior describes accepted resolutions creating solved examples and migration hints. Clarify whether rejected changes are actually ingested or stored, since this affects users’ expectations about what code reaches the backend.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In
`@docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc`
at line 13, Update the Solution Server description to state only the documented
behavior: accepted resolutions generate solved examples and migration hints that
inform future prompts. Remove or qualify the claim that rejected code changes
are evaluated, ingested, or stored unless that behavior is explicitly documented
elsewhere.
🔒 Security & Privacy | 🟠 Major | ⚡ Quick win
Describe the proxy and credential path consistently.
For the Solution Server flow, the supplied deployment contract requires the LLM proxy, which consumes the cluster-managed secret server-side. Remove the “directly to the LLM” wording and replace “credentials are routed through the proxy” with wording that explains requests are proxied while secrets remain in the cluster.
Also applies to: 19-19, 27-27
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In
`@docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc`
at line 13, Update the Solution Server flow description and the corresponding
references at the other indicated occurrences to remove direct-to-LLM wording.
State that requests are proxied through the LLM proxy and credentials or secrets
remain managed within the cluster and are consumed server-side; keep the
surrounding integration behavior unchanged.
|
|
||
| The Solution Server is an independent backend component that builds a collective memory of your migration projects. Your editor extension communicates directly with the large language model (LLM) or routes requests through an LLM proxy. The Solution Server evaluates your accepted and rejected code changes to extract additional insights and resolution patterns. It feeds these insights back to the extension to enhance the prompt context for future LLM solution requests. | ||
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| {mta-dl-plugin} uses Retrieval Augmented Generation (RAG). RAG produces context-based resolutions for issues in your code. By using RAG, the extension improves the context shared with the LLM. |
Contributor
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
Attribute resolution generation to the LLM, not RAG.
RAG retrieves and augments context; the LLM uses that context to generate the resolution. Rephrase this sentence to avoid implying that RAG itself produces code fixes.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In
`@docs/topics/developer-lightspeed/con_solution-server-and-large-language-model-integration.adoc`
at line 15, Update the RAG description in the developer Lightspeed integration
documentation to state that RAG retrieves and augments context, while the LLM
uses that context to generate issue resolutions. Rephrase the sentence without
implying that RAG itself produces code fixes.
Collaborator
Author
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Suggested change
| {mta-dl-plugin} uses Retrieval Augmented Generation (RAG). RAG produces context-based resolutions for issues in your code. By using RAG, the extension improves the context shared with the LLM. | |
| By using Retrieval Augmented Generation (RAG), {mta-dl-plugin} produces context-based code resolutions. The context improves as developers accept more migration fixes over time. |
Contributor
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@mpershina, this is closer, but it still leaves the LLM’s role implicit. To make the distinction unambiguous, I suggest:
By using Retrieval Augmented Generation (RAG), {mta-dl-plugin} retrieves and augments context for the LLM, which generates context-based code resolutions. The context improves as developers accept more migration fixes over time.
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Tracked under MTA-6525.
Preview: https://mpershina.github.io/Previews/MTA/SS-LLM-integration.html#solution-server_dl-context
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