AI Photography Assistant - Hermes Discovery Experiment (Jesslyn's Workflow)

Status: Experimental / Discovery Project Primary Goal: Test the effectiveness and limitations of persistent AI agents (via Hermes) in a real creative workflow. Secondary Goal: Explore whether this can provide practical value to a professional photographer (Jesslyn's business).

Date Started: 2026-06-17 Owner: Leonard / Tony

Key Reference: Odysseus Deep Research Report - The Dawn of Persistent AI Agents in Professional Photography (rp-222259c2ba04)

This report (accessed via provided credentials) validates our direction, expands on the 8 use cases, and explicitly defines "Hermes-style persistence." New insights have been integrated below.

Core Concept

A Hermes-based persistent AI partner that learns a photographer’s style, preferences, and business operations. It acts as a creative second brain across pre-shoot, on-set, post-production, and admin.

This is not primarily about building a product. It is about discovery: What does "persistent" actually mean in practice? Where does it add value? Where does it break? How much human oversight is truly required?

Key Capabilities to Explore

Area	Potential Hermes Role	Persistence Questions to Test
Pre-Shoot	Personalized shot lists, lighting plans from briefs + past work.	Can it recall style from previous shoots without re-prompting?
Post-Production	Culling, tagging, edit suggestions, gallery sequencing.	Does style memory improve suggestion quality over multiple sessions?
Business	Emails, proposals, invoicing, scheduling via Leonard-style automation.	How reliably can admin tasks run with state carried forward?
On-Set	Voice co-pilot suggestions.	Can short-term state + long-term memory combine for useful real-time help?

Experimental Focus: Persistence (Updated with Odysseus Report Insights)

This project will specifically stress-test Hermes persistence mechanisms, aligned with the five pillars of Hermes-style persistence identified in the Odysseus report:

Cross-Shoot Memory — Remember actual style decisions from previous jobs (not just static presets). Dynamic memory that evolves (e.g., client-specific preferences like warmer tones for certain portraits).
State + Context Across Workflow Stages — Understand interconnectedness: pre-shoot decisions inform post-production; client feedback informs future culling. Example: Prioritize candid shots based on pre-shoot notes and adjust retouching style accordingly.
Sub-Agent Coordination — "Culling agent talks to retouching agent talks to delivery agent." Seamless information flow and coordinated decision-making across modules.
Self-Improvement from Corrections Over Time — Continuous learning from human overrides and manual adjustments. Aligns with emerging tools like Aftershoot's self-improving profiles.
Local-First + Explainable Decisions — Photographers must understand why an AI made a choice. Maintains creative control, supports client explanations, and keeps sensitive data private.

Hermes Capabilities to Leverage:

Artifact-based memory (long-term style bible, client history, decisions)
Structured state files for ongoing projects
Sub-agent handoff with context passing
Self-improvement from human feedback/corrections
Cross-session recall (days/weeks apart)
Hybrid local (MacBook) + VPS execution

Success will be measured by:

Reduced need to re-explain style/preferences over time
Measurable time savings on repetitive tasks
Quality of outputs (human judgment)
Number and type of interventions required
Agent's ability to surface relevant past work without explicit search
Demonstrable application of the 5 pillars above

Report Recommendation: Prioritize deep workflow mapping of Jesslyn's actual process before building specific agents. Ground development in real needs.

Realistic Assessment (from original proposal + report)

Upsides:

Time savings on repetitive tasks (culling, admin)
Deep style learning via persistent memory
Strong privacy (local-first)
Extensible skills hub
Hybrid MacBook + VPS setup
Potential for genuine self-improvement and cross-workflow intelligence

Downsides & Limitations (to be tested):

Requires human oversight (especially creative decisions)
Slower performance on older hardware for vision tasks
Initial setup and tuning effort
Not fully autonomous
Integration may need debugging
Ethical risks (authenticity, bias, copyright, privacy) — must be addressed transparently

Project Principles

Discovery first, utility second.
Log everything: what worked, what didn't, why.
Start narrow, expand only after evidence.
Use real workflows (Jesslyn's business) for validity.
Keep it grounded — no hype about full replacement of the photographer.
Incorporate advanced memory concepts (e.g., Retrieval-Augmented approaches, ACAN-style attention) where they enhance Hermes capabilities.

Next Steps (Discovery-Oriented) — Updated with Report

Map Jesslyn’s actual workflow in detail (pre-shoot → on-set → post → admin). This is the report's #1 strategic recommendation before technical work.
Ingest sample briefs, past shoots, and style references into a persistent profile (start with top candidates: retouching style learning + culling).
Build minimal Phase 1 agent focused on one high-persistence area (e.g., Personal Style Learning for Retouching or Automated Culling, per report).
Explicitly implement/test the 5 Hermes-style persistence pillars.
Run controlled tests across multiple "sessions" (simulated and real).
Document findings in this repo (what persisted, what required re-instruction, surprises, alignment with report insights).
Consider ethical guardrails and explainability from the start.

Parent research: Ty_Tech/research
Odysseus Report: research/odysseus-report-rp-222259c2ba04.md (full integration of new research)
Previous use cases baseline: research/use-cases-qwen.md
Hermes capabilities to leverage: artifacts, memory, state tracking, sub-agents, skills, cron, tool integration, local-first architecture.

This experiment will help answer: In what ways can persistent agents actually compound value in creative professional work?

New from Odysseus Report: Strong validation of our focus. Recommends starting with workflow mapping + the two highest-potential use cases. Introduces concrete 5-pillar framework and memory architecture ideas for implementation.

README.md Unescape Escape