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◢ Chapter 05B · Case Study

The agent flow behind the briefing.

A 7-stage pipeline from raw signal sources to LinkedIn publish — with the LinkedIn Post Generator engineered as Fan-Out + Reflection to solve the hook problem. This is what a PM-led agent design looks like end to end.

7
Stages
3
Patterns used
2
Human touchpoints

The 7-stage pipeline

Click any stage to expand its input/output, design notes, failure modes, and the checkpoints generated there. The pain point is auto-opened.

◢ Checkpoint legend — what gets generated where
Fan-Out

Where N parallel generators run with distinct strategies — diversity, not redundancy.

Stages: 5
Reflection

Where a critic agent scores output against a rubric and triggers regeneration if it falls short.

Stages: 5 · 7
Contract Spec

Where a typed input/output schema (and rubric) gates what passes between stages.

Stages: 5 · 6 · 8
01
#

Newsletters, RSS feeds, Twitter/X lists, Slack channels — wherever signal-worthy AI PM news surfaces.

02
#

Reads raw inputs, identifies items that qualify as AI PM signals, and scores relevance.

03
#

Removes signals published in prior editions, formats survivors in the 📌 numbered style.

04
#

Generates 3 variants with different hook angles, critic scores each against a rubric, surfaces the best to human.

Fan-out: 3 parallel generators
Hook A: Surprising claimHook B: Trend readHook C: Contrarian takeCritic scores all 3
Input
Final signal list + voice guide + prior posts + brand rules
Output
3 scored variants, ready for human selection
Design note
Fan-Out gives creative diversity. Reflection enforces quality before human sees anything. This is the stage that costs the most time today.
Failure modes
  • ·All 3 variants using the same hook angle
  • ·Generic hook ("AI is changing things")
  • ·Critic missing real tone problems while nitpicking style
05
#

Generates 3 variants with different hook angles, critic scores each against a rubric, surfaces the best to human.

◢ Checkpoints generated at this stage
Contract spec generated here

This is the stage that gets the full Post Generator Contract — input/output schema, weighted critic rubric, failure modes. See the Contract tab.

Fan-Out happens here

3 parallel generators run with predefined hook strategies (surprising claim · trend read · contrarian take). One inference call per variant.

Reflection happens here

Critic agent scores each variant against the rubric (Hook 30% · Voice 25% · Signal 20% · Format 15% · CTA 10%). Below 7.5 weighted → regenerate.

Fan-out: 3 parallel generators
Hook A: Surprising claimHook B: Trend readHook C: Contrarian takeCritic scores all 3
Input
Final signal list + voice guide + prior posts + brand rules
Output
3 scored variants, ready for human selection
Design note
Fan-Out gives creative diversity. Reflection enforces quality before human sees anything. This is the stage that costs the most time today.
Failure modes
  • ·All 3 variants using the same hook angle
  • ·Generic hook ("AI is changing things")
  • ·Critic missing real tone problems while nitpicking style
06
#

Rahul picks the preferred variant — or approves the auto-selected top-scorer if score ≥ 8.0.

07
#

Applies any selection edits, finalises spacing, emoji, and CTA. One lightweight reflect pass.

08
#

One-click final approval. Posts to LinkedIn via API or clipboard. Logs to edition archive.