- Developers
- Learn advanced techniques for building autonomous AI systems that can write and manage production-ready software.
- Business
- Understand how AI can automate software development, improving efficiency and potentially reducing costs for enterprise applications.
This interview provides a detailed look at advanced techniques for autonomous software development, a key area for AI advancement.
Agent Swarms and Knowledge Graphs for Autonomous Software Development with Siddhant Pardeshi - #763
Audio streams from Sam Charrington. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
On The TWIML AI Podcast, Sid Pardeshi, co-founder and CTO of Blitzy, discusses building autonomous software development systems. He contrasts AI-assisted coding with true autonomy, emphasizing that code is a commodity and acceptance metrics include security, standards, and maintainability. The episode explores Blitzy’s hybrid graph-plus-vector approach for navigating codebases and orchestrating large swarms of AI agents for collaborative analysis and task execution.
This episode explores how AI can build software from start to finish, not just help humans write it. It uses many AI agents working together, like a team, to understand code and complete complex tasks, aiming for secure and maintainable software.
This episode offers deep insights into the practical challenges and advanced techniques for creating truly autonomous AI software development. It is for engineers and technical leaders aiming to move beyond AI-assisted coding to end-to-end automation. The discussion on agent swarms, knowledge graphs, and real-world evaluations connects directly to current frontier lab efforts in scaling AI capabilities for complex, production-level tasks, particularly in enterprise software.
- HostSam Charrington
- GuestSiddhant Pardeshico-founder and CTO, Blitzy
- Guest: Siddhant Pardeshi, co-founder and CTO of Blitzy
- Title: Agent Swarms and Knowledge Graphs for Autonomous Software Development
- Show: The TWIML AI Podcast
- Length: 76 minutes
- Key Concept: Code is a commodity; acceptance is the real metric (security, standards, tests, maintainability).
In this episode, Sid Pardeshi, co-founder and CTO of Blitzy, joins us to discuss building autonomous development systems able to deliver production-ready software at enterprise scale. Sid contrasts AI-assisted coding with end-to-end autonomy, arguing that “code is a commodity” and acceptance is the real metric—security, standards, tests, and maintainability included. We explore Blitzy’s hybrid graph-plus-vector approach, which grounds agents and combines semantic signals with keyword search to navigate large repositories efficiently. Sid breaks down context and agent engineering, how effective context windows have plateaued, and why dynamic agent personas, tool selection, and model-specific prompting matter at scale. He details their orchestration of large swarms of AI agents to collaboratively analyze codebases, plan tasks, and execute complex tasks in parallel. We also dig into why Agents.md and flat memories break down, storing feedback in the knowledge graph, and building real-world evals beyond leaderboards to choose the right model for each task.
The complete show notes for this episode can be found at https://twimlai.com/go/763.
- Agent Swarms
- Siddhant Pardeshi
- Many AI agents working together as a team to achieve a common goal.
- Knowledge Graphs
- Siddhant Pardeshi
- A way to represent information and relationships between data, like a smart network of facts.
- Hybrid graph-plus-vector approach
- Siddhant Pardeshi
- Combining two methods: knowledge graphs for structure and vector search for meaning, to better understand code.
Why Jev Is Changing How We Build With AI with Diogo Almeida - #779
WithDiogo Almeida
From Voice Agents to AI Avatars with Alexander Smola - #777
WithAlexander Smola
Questions people ask
- What is Blitzy's approach to navigating large code repositories?
- Blitzy uses a hybrid graph-plus-vector approach. This grounds agents and combines semantic signals with keyword search for efficient navigation.
- How does Blitzy handle complex tasks with AI agents?
- They orchestrate large swarms of AI agents to collaboratively analyze codebases, plan tasks, and execute complex tasks in parallel.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Sam Charrington.

