The Use of AI Agents in Noograms
By Celso Singo Aramaki + AI (updated: January 2026)
Introduction
Between 2024 and early 2026, “AI agents” shifted from a buzzword to a more practical set of tools: LLMs connected to retrieval, structured memory, and external tools, coordinated through workflows and evaluation checks. In creative production, the most reliable use is not autonomous storytelling, but assisted development—helping authors test scenes, maintain continuity, and iterate faster without losing control of voice and intent.
In Noograms, AI agents are used as a development layer around the graphic novel: a way to support character consistency, research, dialogue exploration, and narrative structure.
What “AI agents” means in 2026
In this context, an agent is best understood as:
- a language model guided by role instructions (e.g., “character voice tester,” “continuity checker,” “editor”)
- connected to retrieval (so it references the project’s canon instead of guessing)
- equipped with tool use (structured functions for summarizing, cross-checking, outlining, tagging, and compiling)
- constrained by guardrails (what it is allowed to claim, and how it should respond when uncertain)
This is less about “characters becoming alive” and more about making a long-form project easier to manage.
AI agents as character workbenches (not replacements)
Each major character can be represented as an “agent profile” built from structured inputs:
- backstory and timeline
- personality constraints and voice notes
- relationships and conflicts
- beliefs, goals, and contradictions
- known facts (“canon”) vs. unknowns (“open questions”)
Agents then help stress-test the writing process by generating controlled variants of dialogue and reactions under specific scene constraints. Outputs are treated as drafts and prompts for revision—not final character truth.
Learning and evolution: what is real vs. what is simulated
In 2026, it’s possible to create the feeling of character evolution through:
- updating the character’s state after key events (what changed, what they learned, what they now avoid)
- adding new canon notes to the retrieval store
- tracking relationship dynamics (trust, tension, dependency) as structured variables
However, this evolution is typically curated, not automatic. Without careful constraints, “self-learning” behaviors drift, contradict earlier canon, or inflate drama in ways that don’t fit the story. For Noograms, evolution is handled through author-approved updates to character state and canon.
Inter-agent relationships and scene simulation
Multi-agent setups are useful for one thing: testing interaction patterns.
A controlled “scene simulation” can run with:
- a fixed setting and stakes
- strict scene objectives (what must happen)
- turn limits
- grounded relationship states
- a moderator agent that flags contradictions, tone breaks, or implausible shifts
The output becomes a workbench transcript—useful for discovering tension lines, dialogue rhythms, and alternative beats—without deciding the final page for the author.
Technical implementation (practical stack)
A typical 2026 implementation for this kind of work involves:
- RAG (Retrieval-Augmented Generation): a searchable canon store (characters, timeline, locations, themes, prior chapters)
- Structured memory: scene-level state updates and relationship variables stored explicitly
- Tool calling / function interfaces: repeatable actions like “summarize character arc,” “check continuity,” “generate 5 dialogue options in voice,” “compare scene variants,” “extract beats”
- Workflow orchestration: agent pipelines (writer → editor → continuity → style) with logging
- Evaluations and checklists: lightweight tests for contradictions, tone drift, verbosity, or character voice mismatch
This architecture favors repeatability over novelty. The point is consistency and speed, not spectacle.
Role of agents in narrative development
Agents can support narrative development in three grounded ways:
- Continuity discipline
Catch timeline errors, inconsistent motivations, and relationship contradictions early. - Iteration speed
Generate structured alternatives (dialogue variants, beat permutations, scene openings) quickly, then select and rewrite. - Decision support
Provide comparisons: what changes if a scene is moved, shortened, or reframed; what consequences ripple into later chapters.
The plot remains authored. Agents help explore options and reduce friction in revision.
Work
In Noograms, AI agents are used as a practical creative layer: canon-aware assistants that help test scenes, maintain character integrity, and accelerate iteration. The approach is deliberately conservative: constrained inputs, retrieval grounding, explicit state updates, and human editorial control. If the system adds value, it does so quietly—by making long-form storytelling more maintainable and more coherent over time.