Architectural patterns, multi-agent systems, and the future of AI product engineering.
CLI-driven AI agents like Terminal Devin or Claude Code are revolutionary for individual productivity, but struggle in team environments. Discover why structured web control planes with explicit approval gates, multi-repo support, and Jira Forge integration provide the governance engineering organizations actually need.
How we designed HeyHi's self-healing agent loop: isolating code generation inside ephemeral AWS Firecracker microVMs, capturing compiler and test output, and iteratively prompting models until code compiles clean and tests pass 100%.
Why writing code is no longer the bottleneck in software development. We explore how automated specification analysis, Jira subtask decomposition, and automated GitHub PR review comment resolution are shifting human focus from code syntax to high-level product intent.
Over the past two years, terminal-based AI coding agents have transformed how individual developers write code. Commands running directly inside local terminals can edit files, execute tests, and refactor functions with surprising speed. However, as engineering leaders attempt to roll out these tools across teams of 20, 50, or 200 developers, a systemic bottleneck emerges.
Terminal tools operate in the context of a single developer's machine and single repository session. Product managers, tech leads, and QA engineers have zero visibility into what the AI agent is planning or executing until a pull request is submitted—or worse, after unverified code has already been pushed to main.
Modern SaaS applications are rarely contained within a single Git repository. A single user story might require updating a Go microservice, tweaking a React frontend, and updating Terraform configuration files. CLI agents tied to a local working directory cannot natively orchestrate across multiple repositories while maintaining consistent state across all three.
This is why we built **HeyHi** as a visual control plane rather than a terminal script. By ingesting specifications from product tools, synchronizing directly with Atlassian Jira in ADF format, running builds in KMS-encrypted sandboxes, and orchestrating across backend, frontend, and infrastructure repositories, HeyHi turns AI development into a transparent, team-wide system of record.