---
id: video/zQg48VT0crU
type: video
title: "Episode 522: What Was Your Eureka Moment with AI? Smarter Workflows for BC Development"
summary: "Dynamics Corner episode 522 is a practitioner conversation about using AI models for AL development: benchmarking models on AL tasks, gaps in AL syntax knowledge, multi-model and agentic workflows with Claude Code and MCP servers, and shifting review effort from code to tests."
tier: community
language: en
tags:
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  - al development
  - benchmarking
  - model testing
  - developer tooling
  - ai safeguards
  - al syntax
  - code generation
  - claude code
  - mcp servers
  - al-go
  - token usage
system: development
review:
  state: reviewed
  by: opus
  at: "2026-10-07T23:19:38.338Z"
  flags: []
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    title: "Episode 522: What Was Your Eureka Moment with AI? Smarter Workflows for BC Development"
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    quote: the models are getting so good now that I need to redo my entire test set for like uh the central gauge thing where
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    quote: some of their um safeguards are triggered by my simple AL tests like oh no this is uh dangerous I am not allowed to
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    quote: I don't do that because what I want to test is the model's own training data It's like the baseline
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    quote: the GPT models have a sort of an issue with interfaces in AL. It's not always they know about that concept in AL languages
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video_id: zQg48VT0crU
channel: yt-dynamicscorner
source_name: Dynamics Corner podcast
url: https://www.youtube.com/watch?v=zQg48VT0crU
published_at: "2026-07-14T15:31:00.000Z"
duration_s: 3565
captions: derived
audience:
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chapters:
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    title: Introduction and BC Tech Days discussion
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    title: Guest introduction - Tom, MVP, and developer tooling focus
  - t: 318
    title: AI model improvements and benchmarking challenges
  - t: 420
    title: Fable model safeguards and fallback mechanisms
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    title: Benchmarking methodology and baseline testing
  - t: 696
    title: AL language knowledge gaps in AI models
  - t: 849
    title: Measuring model awareness of AL syntax and features
  - t: 987
    title: Using multiple AI models for different development tasks
  - t: 1170
    title: Cost optimization and workflow automation with agents
  - t: 1312
    title: Advisory models and Claude Code features for development
  - t: 1454
    title: Setting up agentic workflows and MCP servers for developers
  - t: 1696
    title: Creating custom MCP servers for debugging and AI as co-founder
  - t: 1863
    title: Rethinking software development value and problem-solving paradigm
  - t: 2042
    title: Gotchas in AI-assisted AL development and shift to test verification
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  - name: Multi-model workflow orchestration
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  - name: PAL MCP for consensus feedback
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    status_source: video
  - name: Automatic code compilation and testing in agentic workflows
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  - name: AL MCP server integration with Claude
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    status_source: video
  - name: Future AI-native development without language visibility
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    status_source: video
  - name: AI abstraction layer for financial postings
    status: unclear
    t: 2384
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    status_source: video
  - name: Local model hosting for reduced token costs
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    status_source: video
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quotes:
  - t: 339
    text: the models are getting so good now that I need to redo my entire test set for like uh the central gauge thing where
    check: exact
  - t: 365
    text: now it's more about the harness. The models are even not the top models are like good enough
    check: exact
  - t: 387
    text: some of their um safeguards are triggered by my simple AL tests like oh no this is uh dangerous I am not allowed to
    check: exact
  - t: 683
    text: I don't do that because what I want to test is the model's own training data It's like the baseline
    check: exact
  - t: 710
    text: the GPT models have a sort of an issue with interfaces in AL. It's not always they know about that concept in AL languages
    check: exact
---

# Episode 522: What Was Your Eureka Moment with AI? Smarter Workflows for BC Development

> Dynamics Corner episode 522 is a practitioner conversation about using AI models for AL development: benchmarking models on AL tasks, gaps in AL syntax knowledge, multi-model and agentic workflows with Claude Code and MCP servers, and shifting review effort from code to tests.

[Watch on YouTube](https://www.youtube.com/watch?v=zQg48VT0crU) · Dynamics Corner podcast · 2026-07-14 · 59:25 · tier community · reviewed (checked by Opus)

## Overview

In this podcast episode a developer-tooling MVP (Tom) describes benchmarking AI models on AL tasks. He says the models now score so well that his test set is saturated and needs redoing, and that the harness matters more than the model. He also covers safeguards in the Fable model that triggered on simple AL tests, and knowledge gaps such as GPT models and interfaces, since AL is a younger language than TypeScript or Python.

The discussion then moves to workflows: routing planning, review and coding to different models, an advisory model in Claude Code, the PAL MCP for cross-model feedback, and an AL MCP server that lets Claude publish to a sandbox, run tests and fix errors. It ends with views on test-first verification, local and open-source models for lower token costs, per-use pricing, and how far developers will still look at code in future.

## Key points

- Benchmarking of AL tasks shows top models score near perfectly, so the test set is saturated and new tests and metrics are needed to tell models apart.
- Fable safeguards were triggered by simple AL tests and could fall back to Opus or other models, which makes measuring Fable's own ability harder.
- GPT models are reported to have trouble with AL interfaces. Models trained on older AL syntax miss newer features.
- Guideline markdown files added to prompts can raise all models to the same AL baseline. Benchmark results split by concept (interfaces, tables, records) show where context is needed.
- In Claude Code the guest uses Opus to write a spec, Fable as advisor to review it, the PAL MCP to get consensus from GPT and Gemini, and Sonnet for coding to keep costs down.
- His own ISV pipeline has 13 stages, each with a hand-picked model (Opus for planning). Junior-level DevOps tasks are tagged and come back as pull requests about 8 hours later. This may not suit partners or VS Code users.
- With an AL MCP server, Claude can compile, publish to a sandbox, run tests, fix failures and re-test. The speaker calls this the point where the benefit becomes clear.

## Chapters

- [0:00](https://www.youtube.com/watch?v=zQg48VT0crU&t=0s) Introduction and BC Tech Days discussion
- [3:32](https://www.youtube.com/watch?v=zQg48VT0crU&t=212s) Guest introduction - Tom, MVP, and developer tooling focus
- [5:18](https://www.youtube.com/watch?v=zQg48VT0crU&t=318s) AI model improvements and benchmarking challenges
- [7:00](https://www.youtube.com/watch?v=zQg48VT0crU&t=420s) Fable model safeguards and fallback mechanisms
- [9:13](https://www.youtube.com/watch?v=zQg48VT0crU&t=553s) Benchmarking methodology and baseline testing
- [11:36](https://www.youtube.com/watch?v=zQg48VT0crU&t=696s) AL language knowledge gaps in AI models
- [14:09](https://www.youtube.com/watch?v=zQg48VT0crU&t=849s) Measuring model awareness of AL syntax and features
- [16:27](https://www.youtube.com/watch?v=zQg48VT0crU&t=987s) Using multiple AI models for different development tasks
- [19:30](https://www.youtube.com/watch?v=zQg48VT0crU&t=1170s) Cost optimization and workflow automation with agents
- [21:52](https://www.youtube.com/watch?v=zQg48VT0crU&t=1312s) Advisory models and Claude Code features for development
- [24:14](https://www.youtube.com/watch?v=zQg48VT0crU&t=1454s) Setting up agentic workflows and MCP servers for developers
- [28:16](https://www.youtube.com/watch?v=zQg48VT0crU&t=1696s) Creating custom MCP servers for debugging and AI as co-founder
- [31:03](https://www.youtube.com/watch?v=zQg48VT0crU&t=1863s) Rethinking software development value and problem-solving paradigm
- [34:02](https://www.youtube.com/watch?v=zQg48VT0crU&t=2042s) Gotchas in AI-assisted AL development and shift to test verification

## Features

| Feature | Status | At |
|---|---|---|
| Central gauge benchmarking tool | status not stated | [5:39](https://www.youtube.com/watch?v=zQg48VT0crU&t=339s) |
| Fable model with safeguard fallback | status not stated, demoed | [6:16](https://www.youtube.com/watch?v=zQg48VT0crU&t=376s) |
| Microsoft AL harness for model evaluation | status not stated | [9:56](https://www.youtube.com/watch?v=zQg48VT0crU&t=596s) |
| AL language syntax knowledge gaps in AI models | status not stated | [11:50](https://www.youtube.com/watch?v=zQg48VT0crU&t=710s) |
| Model-agnostic baseline augmentation through guidelines | status not stated | [12:25](https://www.youtube.com/watch?v=zQg48VT0crU&t=745s) |
| Subsection-based model capability reporting | status not stated | [14:32](https://www.youtube.com/watch?v=zQg48VT0crU&t=872s) |
| Advisory model selection in Claude Code | status not stated | [21:52](https://www.youtube.com/watch?v=zQg48VT0crU&t=1312s) |
| Multi-model workflow orchestration | status not stated, demoed | [17:44](https://www.youtube.com/watch?v=zQg48VT0crU&t=1064s) |
| PAL MCP for consensus feedback | status not stated | [18:39](https://www.youtube.com/watch?v=zQg48VT0crU&t=1119s) |
| Automatic code compilation and testing in agentic workflows | status not stated, demoed | [24:48](https://www.youtube.com/watch?v=zQg48VT0crU&t=1488s) |
| AL MCP server integration with Claude | status not stated, demoed | [26:12](https://www.youtube.com/watch?v=zQg48VT0crU&t=1572s) |
| Simplified MCP server setup via AI | status not stated | [27:26](https://www.youtube.com/watch?v=zQg48VT0crU&t=1646s) |
| Custom MCP for BC performance and debugging | status not stated, demoed | [28:16](https://www.youtube.com/watch?v=zQg48VT0crU&t=1696s) |
| Test-first code verification approach | status not stated | [35:37](https://www.youtube.com/watch?v=zQg48VT0crU&t=2137s) |
| Future AI-native development without language visibility | status not stated | [39:05](https://www.youtube.com/watch?v=zQg48VT0crU&t=2345s) |
| AI abstraction layer for financial postings | status not stated | [39:44](https://www.youtube.com/watch?v=zQg48VT0crU&t=2384s) |
| Local model hosting for reduced token costs | status not stated | [49:36](https://www.youtube.com/watch?v=zQg48VT0crU&t=2976s) |
| Per-use pricing for GitHub Copilot and AI services | status not stated | [51:25](https://www.youtube.com/watch?v=zQg48VT0crU&t=3085s) |
| AI model harness for workflow management | status not stated | [53:22](https://www.youtube.com/watch?v=zQg48VT0crU&t=3202s) |
| Local open source AI models | status not stated | [52:31](https://www.youtube.com/watch?v=zQg48VT0crU&t=3151s) |

## AL objects mentioned

As heard in the captions. A name that matches one object page by exact type and name links to it; the others stay as named.

- codeunit "test code unit" at [9:27](https://www.youtube.com/watch?v=zQg48VT0crU&t=567s)
- other "PAL" at [18:39](https://www.youtube.com/watch?v=zQg48VT0crU&t=1119s)
- other "AL MCP" at [24:14](https://www.youtube.com/watch?v=zQg48VT0crU&t=1454s)
- other "BC quality" at [24:14](https://www.youtube.com/watch?v=zQg48VT0crU&t=1454s)
- other "AL tool" at [24:24](https://www.youtube.com/watch?v=zQg48VT0crU&t=1464s)
- other "ALC.exe" at [24:24](https://www.youtube.com/watch?v=zQg48VT0crU&t=1464s)

Not found in BC28-30: codeunit "test code unit".

## Quotes

- [5:39](https://www.youtube.com/watch?v=zQg48VT0crU&t=339s) "the models are getting so good now that I need to redo my entire test set for like uh the central gauge thing where"
- [6:05](https://www.youtube.com/watch?v=zQg48VT0crU&t=365s) "now it's more about the harness. The models are even not the top models are like good enough"
- [6:27](https://www.youtube.com/watch?v=zQg48VT0crU&t=387s) "some of their um safeguards are triggered by my simple AL tests like oh no this is uh dangerous I am not allowed to"
- [11:23](https://www.youtube.com/watch?v=zQg48VT0crU&t=683s) "I don't do that because what I want to test is the model's own training data It's like the baseline"
- [11:50](https://www.youtube.com/watch?v=zQg48VT0crU&t=710s) "the GPT models have a sort of an issue with interfaces in AL. It's not always they know about that concept in AL languages"

## Disclaimers in the video

- [7:00](https://www.youtube.com/watch?v=zQg48VT0crU&t=420s) subject-to-change: depending on how soul or the next opening eye model if they are going to have safeguards due it's going ...
- [8:02](https://www.youtube.com/watch?v=zQg48VT0crU&t=482s) subject-to-change: it could be like uh depending on how soul or the next opening eye model if they are going to have safeguards
- [22:05](https://www.youtube.com/watch?v=zQg48VT0crU&t=1325s) preview: that's also a new feature I don't how many knows about in claw code you can set an advis advisory model
- [23:27](https://www.youtube.com/watch?v=zQg48VT0crU&t=1407s) subject-to-change: there was a bug in cloud code that fixed that now uh there was actually an an optin. They by ...
- [47:53](https://www.youtube.com/watch?v=zQg48VT0crU&t=2873s) coming-later: I'm going to add a lot of local models both uh open models hosted and like open router but also ...
- [49:01](https://www.youtube.com/watch?v=zQg48VT0crU&t=2941s) subject-to-change: I haven't tested it yet in in real A programming. It's only been in small tests here and there. nothing concrete yet
- [52:31](https://www.youtube.com/watch?v=zQg48VT0crU&t=3151s) coming-later: my prediction for 2027 will be you'll see a lot of these local models getting or these open source local ...
- [55:17](https://www.youtube.com/watch?v=zQg48VT0crU&t=3317s) subject-to-change: they pushed it out till July 12th

Presenters (as heard): Chris, Brad, Torbin Leth, Tom.