Concho AI turns enterprise codebases into a knowledge layer for AI agents
Concho AI today introduced its flagship platform, an artificial intelligence platform that understands software development and application work, providing deep semantic understanding and organization that enable developers and AI agents to transform and modernize massive, sprawling codebases into something manageable. "Its job is to provide an intelligence level that you would normally get from a [... ] The post Concho AI turns enterprise codebases into a knowledge layer for AI agents appeared first on SiliconANGLE.
Key Takeaways
- "Its job is to provide an intelligence level that you would normally get from a high-priced consultant or an architect to anyone who wants to know what the code says," Chief Technology Officer Bruce Henderson told SiliconANGLE in an exclusive interview.
Now that the software industry is stepping into agentic AI, developers are discovering that agents can generate enormous volumes of code.
- Also, as time passes in any codebase, something even more terrible happens: developers come to dislike writing documentation, or simply neglect to do so.
It's common for older codebases to contain "shadow" code that exists for a purpose but its operation and value may have been lost when developers left the company years ago.
- It's a middle "fact layer" that acts as a knowledge graph plugin and lets users explore what an application means, including its architecture and business behavior, rather than inspecting individual files and procedures.
This information is then exposed to tools such as Anthropic PBC's Claude through the Model Context Protocol.
- The resulting fact model can return a compact architectural overview or descend into a deep dive, backed by specific, sourced evidence.
In the field, multiple customers are already using Concho to understand their systems and build better apps.
- "It's been through a number of changes, and it is very tangled and complex.
Stats & Key Facts
- #"They're north of 12 million lines of code in their core offering, and it's been around for a while," Henderson said.
"Its job is to provide an intelligence level that you would normally get from a high-priced consultant or an architect to anyone who wants to know what the code says," Chief Technology Officer Bruce Henderson told SiliconANGLE in an exclusive interview. Now that the software industry is stepping into agentic AI, developers are discovering that agents can generate enormous volumes of code. For greenfield development and smaller repositories, they work well.
Frontier models and industry-standard context window sizes let them "see" entire projects at a time, enabling them to gather enough data and knowledge to do significant work. However, Henderson argues, as projects age, as they reach millions of lines of code across several languages and have passed through multiple generations of developers, this can erode the ability for AI agents to work efficiently and accurately. Also, as time passes in any codebase, something even more terrible happens: developers come to dislike writing documentation, or simply neglect to do so.
It's common for older codebases to contain "shadow" code that exists for a purpose but its operation and value may have been lost when developers left the company years ago. "What you're looking at is a combination of business-critical system and archaeological marvel," Henderson explained. Generative AI has made producing new code inexpensive.
For more details please read the original article at SiliconANGLE AI.
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