Devs manually seeded context for complex tasks. Agents now retain memory for weeks, working independently after a single session.
A developer of secure open source software manages large internal codebases and a constant influx of upstream package updates requiring rigorous engineering review at every step.
Engineering tasks require deep context from multiple repositories, but standard AI tools lost memory during complex workflows spanning brainstorming...
“Like most real engineering problems, our use cases require a lot of context from various upstream sources and across multiple internal repositories. Specifically at Chainguard, where we juggle both our internal codebases and an ever-growing chunk of the open source ecosystem.”
Chainguard's Software engineering automation is part of this use case:
Secure software supply chain platform and zero-CVE container images.
AI software development agents for automating engineering workflows.
Related implementations across industries and use cases
Tracing vulnerabilities across scattered tools took days. Now, AI agents autonomously synthesize threat data and draft precise fixes.
Contractors drowned in ticket backlogs. AI agents now crawl digital surfaces to spot patterns and auto-file takedowns 24/7.
IT and security teams juggled 40 browser tabs and spent days provisioning access. Now, AI agents execute these workflows in minutes.
Context-blind prompts slowed development. An AI agent now reasons across the full codebase to plan and execute complex tasks instantly.
Talent scarcity slowed delivery. Trained on platform coursework, an AI agent now builds apps in days and ranks as the team's #2 developer.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Devs manually seeded context for complex tasks. Agents now retain memory for weeks, working independently after a single session.
A developer of secure open source software manages large internal codebases and a constant influx of upstream package updates requiring rigorous engineering review at every step.
Engineering tasks require deep context from multiple repositories, but standard AI tools lost memory during complex workflows spanning brainstorming...
“Like most real engineering problems, our use cases require a lot of context from various upstream sources and across multiple internal repositories. Specifically at Chainguard, where we juggle both our internal codebases and an ever-growing chunk of the open source ecosystem.”
Chainguard's Software engineering automation is part of this use case:
Secure software supply chain platform and zero-CVE container images.
AI software development agents for automating engineering workflows.
Related implementations across industries and use cases
Tracing vulnerabilities across scattered tools took days. Now, AI agents autonomously synthesize threat data and draft precise fixes.
Contractors drowned in ticket backlogs. AI agents now crawl digital surfaces to spot patterns and auto-file takedowns 24/7.
IT and security teams juggled 40 browser tabs and spent days provisioning access. Now, AI agents execute these workflows in minutes.
Context-blind prompts slowed development. An AI agent now reasons across the full codebase to plan and execute complex tasks instantly.
Talent scarcity slowed delivery. Trained on platform coursework, an AI agent now builds apps in days and ranks as the team's #2 developer.
Scattered spreadsheets couldn't catch AI hallucinations. Now, automated LLM judges evaluate every prompt change to block regressions.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.