## Objectives 🎯

[Nichebiche]

Objectives 🎯

1. AutoGPT should be able to:
   • Handle tasks of arbitrary complexity
   • Recover and continue in case of unexpected intermediary results, e.g. tool failure or unavailability of a non-essential 3rd party service
2. AutoGPT should be optimally productive in terms of achieved results per $ of LLM expense

SWOT 🧭

Strengths 💪

• Exceptions and other problems during tool execution are handled gracefully and fed back to the LLM

Weaknesses 🚧

• Linear planning & stepping
  • Limited historical context, even with compression of the ActionHistory in the prompt
  • Failed execution branches remain in historical memory and take up space

Opportunities 🚀

• Non-linear planning strategies could provide a combination of better (relevant) context retention and recovery on failure
• Nested planning strategies could allow handling tasks of arbitrary complexity

Threats ⚠️

• Nested planning carries a risk of (semi-)infinite recursion
• Context retention could become less relevant as models with lower token cost and larger effective context windows become available
• Nested planning could be the kryptonite that enables AutoGPT to do something scary

Related 👪

• Ability acquisition 💡

Actionables

• 4107

• [ ] Implement execution of multiple actions at once
  • OpenAI supports this natively in their new GPT models since 2023-11-06
• [ ] Implement action piping/chaining (maybe through code bindings/API?)

  • 7142

• [ ] Implement intent → implement → execute → evaluate flow (instead of handling everything except execution in the same one-shot prompt)
  • The “implement” step would be a good place to integrate Procedural Memory.

Notes

• The terms "actions", "commands", "tools" are used interchangeably and refer to the same concept.

Originally posted by @Pwuts in https://github.com/Significant-Gravitas/AutoGPT/discussions/6964

[Pwuts]

As mentioned in the post, this is a duplicate of #6964. If you have something to add to the roadmap item, please comment below it.