research/jesslyn-photographer-workflow/README.md

# 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)](research/odysseus-report-rp-222259c2ba04.md)

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:

1. **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).

2. **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.

3. **Sub-Agent Coordination** — "Culling agent talks to retouching agent talks to delivery agent." Seamless information flow and coordinated decision-making across modules.

4. **Self-Improvement from Corrections Over Time** — Continuous learning from human overrides and manual adjustments. Aligns with emerging tools like Aftershoot's self-improving profiles.

5. **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

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

## Related
- **Parent research**: Tony_tech/research (primary location for this work)
- 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.

**Note**: This folder was migrated/ensured in Tony_tech/research as the primary home for the discovery work (previously also tracked under Ty_Tech/research for testing).