Bito

AI Architect now learns from your Git commit history. Every codebase carries two stories. The code as it exists today and the decisions that shaped it over time.  Until now, AI Architect understood the first one. Now it understands both. Hotspot detection, thematic clustering, contributor patterns, and confidence scores, all extracted from real commits across every indexed repo. Combined with Jira ticket history, AI Architect now grounds every plan, technical design, and code review in why something was built and how it actually changed over time. Learn more here: https://lnkd.in/daA-vx-p

Introducing Bito’s Slack Agent.  Most engineering decisions happen in Slack threads. Plans, tradeoffs, context that never makes it into a doc. Mention @Bito in any thread and it reads the conversation, shared files, referenced Jira tickets, and linked Confluence pages, then responds with a grounded answer powered by AI Architect. → Summarize long threads   → Compare technical approaches   → Pull action items with owners   → Turn an agreed plan into a branch with the code changes  Same AI Architect that powers technical design in Jira, code generation in Cursor and Claude Code, and code reviews on every pull request. Now in the channel where your team decides what to build. Read more here: https://lnkd.in/dgZNweue

Coding agents write code. AI reviews every pull request. Test generation takes seconds. But ... The quality of all that output depends on the software design document that came before it. A vague design document produces vague code. A grounded one produces code that: - respects existing patterns, - avoids known failure points, and - accounts for cross service dependencies. 80% of a senior developer's time goes to non-coding work. Getting the design document right is where code quality actually starts. We wrote about how software design documents shape AI code quality and what teams can do to make them work better with coding agents. Read here: https://lnkd.in/d5dW6bCa

An LLM explored a 450-repo Go codebase and returned the wrong Redis key format because it stopped at the wrong abstraction layer. AI Architect traced the correct key across 2 repos and 4 abstraction layers and verified every segment from source code. Without AI Architect: - RESTAURANT#<restaurantId>#<algoName> - Wrong delimiter, missing prefix, stopped at the DynamoDB layer With AI Architect: - rnr~pk_id~RESTAURANT#<restaurantId>~sk_id~<algoName> - Every segment traced and verified from source The difference between a confident guess and a verified answer traced from source code. In a system processing millions of events daily, that difference is silent cache misses hitting your database. Full technical writeup in comments.

March 2026 marked a new chapter for Bito. We expanded AI Architect beyond grounded code generation into the phase that determines whether the right thing gets built in the first place. Technical design is now powered by AI Architect, directly inside your Jira workflow. 

Stripe's Developer Coefficient found that developers spend only 32% of their time writing code. The rest goes to planning, understanding systems, debugging, maintenance. AI tools made the coding part significantly faster. But that is only 32% of the picture. Bain & Company's research backs this up. Even with AI handling 40% of coding tasks, overall engineering gains have been underwhelming because the work around the code has not caught up. I keep seeing this with Bito's customers too. The feature that takes two weeks to ship does not take two weeks because of coding. It takes two weeks because three different people need to weigh in on feasibility, someone needs to map which services get affected, and the one architect who knows the billing system is in back to back meetings until Thursday. That is tribal knowledge running your planning process. And it does not scale. We have been building toward solving this at Bito. AI Architect's knowledge graph now powers the technical design phase through Jira, combining codebase context with operational history from past tickets. So the design document your team starts with accounts for where things have actually broken before. I wrote about what this costs engineering teams and why wikis and Confluence pages never solved it. Link in comments.

Bito’s AI Architect now extends into technical design and planning through Jira. AI agents accelerated coding. Technical design still lags behind. Senior engineers spend 60 to 70% of their time on work before coding starts. Every new feature creates that same queue at the same desks. AI Architect now gives every engineer system level context the moment an epic is created, combining deep codebase context with operational history from past Jira tickets. AI Architect now provides: → Feasibility analysis   → Technical design documents   → Epic breakdowns   → Proactive risk detection Same knowledge graph now powering technical design, code generation, and code reviews. Read more here: https://lnkd.in/dDDbmDaG

On Elasticsearch's 3.85M line codebase, Claude Code skipped implementation TPUT algorithm because it could not see how the pipeline connects. We ran the same agent on the same task with AI Architect providing system context. Without system context: ➡️ Brute force workaround, 6 files, severe memory risk With AI Architect: ➡️ Genuine multi phase TPUT across 27 files ➡️ Proper pipeline extension and transport actions ➡️ Multi shard test coverage Full writeup in comments.

Your coding agent indexed everything and still gets lost in your codebase. Agents resolve symbols and follow references. They cannot: → model cross service relationships, → trace data flows, or → capture constraints learned through past incidents. That system level understanding is the missing layer in every major coding agent today. Amar Goel wrote about what closes this gap. Read here: https://lnkd.in/e4Rsn5Vn