Exploring Agentic Systems in Software Engineering: Planning, Decision Making, and Environmental Grounding

As we explored building autonomous systems, or 'droids,' to bring autonomy to software engineering, we discovered three key characteristics that define an agentic system: planning, environmental grounding, and decision making - each with its own set of challenges and opportunities.

1. The speaker will discuss practical examples and lessons learned from building "droids," autonomous systems that assist in different stages of the software development life cycle.
2. Factory's mission is to bring autonomy to software engineering.
3. Droids have separate cognitive architectures tailored to specific tasks.
4. Agentic systems, like Factory's droids, have three main characteristics: planning, environmental grounding, and decision-making.
5. Planning involves making decisions about one or more future actions using some criteria and algorithm.
6. Environmental grounding is crucial for agentic systems to interact with external environments through reading and writing.
7. The pseudo common filter in control systems and robotics helps maintain consistent reasoning during plan execution by iterating intermediate reasoning through different plan steps.
8. Subtask decomposition increases the resolution and fidelity of subtasks in a given plan, providing more fine-grained control but introducing more decisions for the system to make.
9. Model predictive control evaluates outcomes of subtasks and enables adaptive replanning based on real-time feedback during plan execution.
10. Explicit plan criteria, such as successful structures or initial states, can lead to strong downstream effects in agentic systems.
11. Decision-making in agentic systems involves consensus mechanisms, explicit/analogical reasoning, fine-tuning, and simulation of decision-making.
12. Consensus mechanisms, like self-consistency and prompt ensembles, improve the accuracy of decisions at the cost of longer inference times.
13. Explicit and analogical reasoning techniques, such as Chain of Thought or checklists, reduce complexity in decision-making by outlining reasoning criteria.
14. Fine-tuning a model with specific data for particular decisions can be effective, especially when decisions are out of distribution.
15. Simulation of decision-making is domain-specific and involves evaluating whether the system makes good decisions when needed.
16. Environmental grounding, or tool use, is an interesting challenge in building AI computer interfaces.
17. Custom AI computer interfaces can streamline workflows that don't exist within the known toolset.
18. Explicit feedback processing is critical for grounding an agent in an external environment, such as parsing logs and providing explanations.
19. Bounded exploration allows agents to gather context about the problem space before diving into the problem.
20. Human guidance is essential when designing interaction patterns between humans and agentic systems.
21. Finding the right balance of bounded exploration time requires evaluation and data collection.
22. Human oversight and autonomy must be balanced in human-agent interactions.
23. Factory is hiring AI Engineers, software Engineers, and go-to-market teams; interested candidates can email the speaker for more information.

Source: AI Engineer via YouTube

❓ What do you think? What is one key takeaway from this presentation about building autonomous systems, and how can it be applied to your own projects or endeavors? Feel free to share your thoughts in the comments!