Persistent Project State & Rule Tracking for ChatGPT (Agent-Like Continuity Across Sessions)
Summary
ChatGPT would become significantly more effective for complex, long-running tasks if users could define persistent rules and structured state that the model maintains and updates across sessions—without requiring repeated manual context injection.
This would allow ChatGPT to function as a stateful working partner, not just a stateless conversational tool.
My use case Problem
ChatGPT currently relies on a generous, but ultimately limited context window, which leads to:
Loss of important rules as conversations grow
Repeated manual re-entry of constraints and project context
Inconsistent handling of long-term structured information
Users working on serious projects end up building manual systems (control blocks, notebooks, summaries) to compensate.
While effective, this introduces:
friction
time overhead
risk of inconsistency
Proposed Solution
Introduce a lightweight system with:
1. Persistent Rule Layer (“Agent Rules”)
Users define rules such as:
“Respect system architecture boundaries”
“Track all timeline events and ensure consistency”
“Maintain a decision log and avoid reintroducing rejected approaches”
These rules:
persist across sessions
are always visible and editable
are automatically applied when relevant
2. Model-Maintained Structured State
Allow the model to create and maintain simple structured artifacts such as:
timeline.log
decisions.md
system_definition.md
constraints.json
The model:
updates these during conversation
references them when generating responses
keeps them compact and relevant
3. Selective Context Loading
Rules are always active
State is loaded only when relevant
Avoids unnecessary context bloat
Real-World Use Cases
1. Computer Vision System Design (Camera Monitoring Project)
In a real-world project designing a CPU-efficient intrusion detection system:
The system is built around strict architectural layers:
Layer 0: input trust (health, illumination, tamper)
Shadow: illumination refinement
Layer 1: nuisance suppression
Layer 2: motion classification
User requirement:
“Never violate layer responsibilities and maintain system consistency.”
Current workaround:
Maintain a control block and system notebook manually
Re-paste context when needed
Periodically correct drift
With persistent rules and state:
The architecture definition is stored once
The model references it automatically
Decisions (e.g., “no heavy ML,” “shadow is refinement only”) are preserved
New suggestions are validated against system rules
Result:
higher consistency
less repetition
reduced design errors
2. Long-Form Novel Writing (Timeline & Continuity)
In a science fiction writing project with complex causality:
User requirement:
“Track all events, character actions, and timelines. Prevent continuity errors.”
Current workaround:
Maintain external notes
Re-explain context to the model
manually check consistency
With persistent state:
The model maintains a timeline.log
Each new scene updates the timeline
The model cross-checks events automatically
Flags inconsistencies in causality or sequence
Result:
dramatically improved continuity
reduced cognitive load on the writer
enables deeper, more complex storytelling
3. Real-Life Constraint-Based Planning
User requirement:
“Ensure all advice respects specific constraints (budget, rules, policies).”
With persistent rules:
constraints are defined once
applied consistently over time
no need to repeat them
Why This Matters
Users are already attempting to simulate this behaviour manually or by using tools like OpenAI Codex for non-coding workflows.
This demonstrates:
strong demand for persistent structured context
applicability beyond coding
a natural evolution toward agent-like systems
Design Principles
User-controlled (no hidden or opaque memory)
Transparent (rules and state are visible and editable)
Efficient (no full context reload each turn)
Optional (does not affect casual usage)
Expected Impact
Reduced need for repeated context injection
Improved long-term and consistency across long sessions
Better support for complex, multi-step multi-session workflows
Expansion of ChatGPT into a true “working partner” for serious tasks
Closing
This feature would unify existing capabilities into a coherent system and enable ChatGPT to lean into becoming a true long-term collaborator.