How to build an AI specialist that selects
rather than summarizes — and why
narrative cognition matters for knowledge work.
canvas-specialists is a bundle of AI specialist agents designed for structured knowledge work. Each specialist has a formally specified output contract, a multi-stage pipeline, and documented guardrails. They chain together: one specialist’s output is the next specialist’s input. Machine-parseable contracts enable reliable handoffs.
The most common pipeline:
This chain produces rigorous, well-cited documents. Sourced findings. Labeled inferences. Full coverage. Inline citations tracing every claim.
But the documents read like what they are:
structured data transformed into prose.
“There are two modes of cognitive functioning,
two modes of thought, each providing
distinctive ways of ordering experience.”
— Jerome Bruner, Actual Minds, Possible Worlds, 1986
Logical. Categorical. Evidence-first. Evaluated by truth and falsehood. This is what the pipeline speaks.
Sequential. Causal. Meaning-making. Evaluated by verisimilitude and coherence. This is what audiences need.
You cannot get from one to the other by reformatting.
It requires structural transformation.
The audience receives a rigorous document in the wrong cognitive mode. They don’t act on it — not because the evidence is weak, but because the format doesn’t engage the system that drives decisions. Stakeholders, boards, customers need to be moved, not just informed.
A human transforms the analysis into a narrative by hand. Traceability disappears. You can’t tell what was selected, what was left out, or why. The editorial judgment that shaped the story is invisible — and unreviewable.
For stakeholder communications, change narratives, and user research synthesis — where the audience needs to be moved, not just informed — there was a gap.
“The Writer covers everything.
The Storyteller selects.”
Selection is the creative act. The Writer surfaces all findings, flags gaps, and produces structured documents with full coverage. The Storyteller is authorized to choose which findings are load-bearing for the narrative arc and which ones to set aside.
What you leave out shapes the narrative
as much as what you include.
| Axis | Signal | Framework / Effect |
|---|---|---|
| Primary Goal | Decision needed | SCQA (answer-first, Minto) |
| Persuasion required | Three-act or Sparkline (Duarte) | |
| Insight to deliver | Kishōtenketsu (twist-based) | |
| Change to communicate | Story Spine (causal chain) | |
| Audience | Expert, time-constrained | Reinforces SCQA |
| Skeptical, needs convincing | Reinforces three-act | |
| General audience | Emotional-first framing | |
| Tone | Trustworthy | Conservative register, evidence-forward |
| Dramatic | Amplified tension, urgency foregrounded | |
| Creative | Experimental sequencing, unexpected angles | |
| Persuasive | Loss aversion activated, audience-as-hero | |
Framework determines structure. Tone determines register. Any framework in any tone. A trustworthy SCQA and a dramatic SCQA are the same shape delivered in different voices.
Stage 3 is where the craft lives. The 7 transformation decisions — dramatic question, protagonist, gap, evidence selection, sequencing, register, peak moment — are all made before a single word of prose is written.
Each one earns a narrative role:
Each one gets an explicit rationale:
Nothing is silently dropped. Every finding from the Analyzer is accounted for.
An editorial audit trail that makes narrative judgment transparent and reviewable.
NARRATIVE SELECTION
——————————————
Dramatic question: Why are Fortune 500 companies generating impressive AI pilots they cannot finish?
Protagonist: Enterprise leadership
Framework: SCQA | Board audience + decision context
INCLUDED FINDINGS
F1: 78% Fortune 500 have AI in production | role: hook
F4: 60% of AI projects fail at pilot stage | role: complication
F5: Root cause is missing exec sponsorship | role: peak
F3: Dedicated AI teams ship 3x faster | role: resolution
OMITTED FINDINGS
F2: 18-month avg prototype-to-production | rationale: off-arc
A reviewer can see exactly what was selected, what was cut, and why — something human writers rarely document.
Inline citations activate the analytical reading mode and suppress narrative transportation — the psychological state where readers lose themselves in a story.
Narrative transportation mediates all narrative persuasion. Interruptions — including citation markers — break absorption and reduce impact.
Adding statistics to a story reduces its emotional impact. More analytical detail is not better narrative.
Provides full traceability without breaking the story. Auditability lives in the editorial record, not in the prose.
<3 findings extracted
>50% omitted as insufficient-evidence
Upstream quality NOT MET
Max 2 revision cycles, then NOT MET.
Contract items verified.
Two tests: a rich AnalysisOutput (5 findings, board audience, trustworthy tone) and a boundary case (single low-confidence tertiary finding, upstream NOT MET). The first produced a clean SCQA narrative. The second correctly returned NOT MET with specific diagnostics.
4 findings included with roles. 1 omitted (off-arc) with documented rationale. SCQA framework selected via 3-axis logic. Clean prose, zero citations. QUALITY THRESHOLD RESULT: MET
Pipeline halted at Stage 2. No protagonist identifiable. No stakes establishable. No narrative fabricated. 6 specific structural failures documented. QUALITY THRESHOLD RESULT: NOT MET
The boundary case didn’t silently produce a weakened story and present it as complete. It halted, named the 6 missing structural elements, and refused to proceed. That’s the design working.
The pipeline gained a new terminal node.
Analytical chain — comprehensive coverage, inline citations
Narrative chain — selective framing, editorial transparency
Same first three steps. Different final specialist depending on the deliverable — document or narrative. The Storyteller speaks Bruner’s other mode.
Cognitive foundation:
Craft principles:
Artifacts:
docs/plans/2026-03-04-storyteller-design.mddocs/02-requirements/epics/03-storyteller.mdspecialists/storyteller/index.mddocs/test-log/storyteller/2026-03-04-storyteller-verification.mdPrimary contributor: Chris Park (design direction, all decisions) with Amplifier AI (research synthesis, implementation, verification)
The Storyteller adds a capability
the pipeline didn’t have:
the ability to select, frame, and structure
findings for narrative impact rather than
comprehensive coverage.
But shipping is not the same
as proving it works in a chain.
That question requires evidence,
not architecture.
Next: “How Do You Know?” — The blind test, the statistical reckoning, and the five-word question that changed everything.
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