As AI-powered development tools continue to transform software engineering, the focus is shifting beyond code generation toward deeper codebase understanding, architectural intelligence, and effective technical planning. In this exclusive interaction, Rajeev Ranjan, Editor, Digital Terminal, speaks with Amar Goel, CEO & Co-founder of Bito, about the growing importance of codebase comprehension, the role of AI Architect in capturing and structuring architectural knowledge, and how AI-driven intelligence is enabling faster, safer, and more reliable software development for modern enterprises.
Rajeev: Most AI tools today focus on code generation. Why do you believe codebase comprehension is becoming the bigger challenge for engineering teams?
Amar: Code generation got useful faster than anyone expected, and that’s exactly what exposed the real bottleneck. The engineering leaders I talk to tell a consistent story: their teams adopted AI coding tools, saw genuine productivity gains on isolated tasks, and then hit a wall. The wall isn’t writing code. It’s understanding the system you’re writing code into.
A large enterprise codebase carries years of architectural decisions, API contracts that predate the current team, and defensive logic encoding operational lessons nobody wrote down. When an AI agent operates without that structural understanding, it reasons from what’s locally visible. A change that touches authentication state, encryption key management, and mail synchronization across five services requires knowing how those systems relate—and that knowledge lives at the architecture level, not the file level.
This is why technical planning and design is where the real leverage is. Before you write a single line of code, you need to understand what you’re building into. That’s the step most AI tools skip entirely. AI Architect produces concrete planning artifacts—epic breakdowns, feasibility analyses, spike analyses—that give engineers and agents the architectural context they need before implementation begins. The difference shows up clearly: on SWE-Bench Pro, state-of-the-art agents resolved fewer than 45% of tasks with full repository access. With AI Architect providing structured codebase context, resolution climbed 39% relative. For tasks requiring changes across ten or more files, the improvement was 4.5x.
Rajeev: Large and complex codebases often carry undocumented architectural knowledge. How does Bito’s AI Architect capture and structure this knowledge to make it useful for developers?
Amar: Most architectural knowledge that keeps a large system coherent lives in the heads of your senior engineers, and nowhere else. Teams discover this when those engineers leave, when a new developer makes a change that looked safe in isolation and breaks three downstream services, or when an AI agent generates code that contradicts a pattern the team established years ago for good reasons.
AI Architect addresses this by building a knowledge graph across your entire codebase and understanding operational knowledge in your issue tracker —whether that spans 50 repositories or 5,000. The distinction from traditional retrieval matters here. Embeddings find code that resembles what you searched for. A knowledge graph captures how your system actually functions: typed relationships across classes, functions, APIs, configurations, and services. Full call chains, dataflow paths, dependency structures, your team’s coding conventions, error handling patterns, and design decisions—all indexed as structured, queryable knowledge. With data from issue trackers like Jira and Linear, the architect really understands operationally what's happening in your codebase. Things like:
· We often forget to put in logging.
· This service has had many hotfixes recently so it's quite unstable.
What makes this practically powerful is the planning artifacts it produces. When a PM files an epic for a new feature, AI Architect can generate a feasibility analysis grounded in the actual architecture: which services are affected, what dependencies exist, where the risk concentrations are. It produces epic breakdowns that map requirements to specific modules and APIs, and spike analyses that identify unknowns before your team invests sprint cycles. The knowledge that previously required weeks of discovery or a conversation with your most tenured engineer becomes available as a structured deliverable in minutes.
Rajeev: Many organizations are adopting AI coding assistants. What limitations do these tools face when they operate without deep system-level context?
Amar: AI coding agents have delivered real value, and the productivity gains are genuine. But as teams push these tools into more complex workflows, a specific limitation shows up. Coding agents today operate file by file—reading what’s visible, inferring relationships from limited context, and producing something that looks architecturally sound. On self-contained tasks, that works. On complex, multi-service changes, it falls short in ways that carry real cost.
Consider an agent fixing an authentication bug without understanding how session state, encryption keys, and downstream service calls interact across four or five services. It produces code that passes local tests, clears review, and breaks in production. The debugging cycle that follows consumes far more senior engineering time than the original task saved.
The deeper issue is that these tools jump straight to implementation without a deep planning phase. That is fine for smaller tasks, but if you have a complicated or meaningful feature you need help with, then the complexity sneaks up on you. They don’t produce a feasibility analysis before generating code.
They don’t break an epic into architectural components before starting work. They don’t identify cross-service risks upfront. That’s the gap AI Architect fills—it gives agents the same technical planning step that a good senior engineer would do before writing code. On SWE-Bench Pro, tasks requiring changes across 15 or more files showed a zero percent resolution rate without system context. With AI Architect, agents resolved tasks in that category for the first time. The difference isn’t smarter code generation. It’s better planning.
Rajeev: Can you explain how Bito’s AI Architect builds and maintains a living knowledge graph of a codebase, especially in large repositories with thousands of modules and dependencies?
Amar: The distinction that matters most is between retrieval and understanding. Most tools index code as text and retrieve snippets that resemble what you searched for. AI Architect builds a structural model of how your system actually functions. It runs deep static analysis across every repository, producing typed relationships across classes, functions, APIs, configurations, and services. It traces full call chains, maps dataflow paths, and surfaces dependency structures that span the entire codebase.
The knowledge graph stays current as your codebase evolves. As your team ships new services, refactors modules, or changes API contracts, the graph updates. A continuously updated structural model compounds in value as your system grows more complex.
What this enables on the planning side is what I’m most excited about. The knowledge graph is the foundation that makes AI Architect’s planning artifacts reliable. When an epic comes in, AI Architect can trace through the graph to produce a feasibility analysis that accounts for every affected service, every dependency chain, every integration point. It generates spike analyses that surface technical unknowns your team would otherwise discover mid-sprint. It creates implementation plans that map directly to the real architecture rather than assumptions about it. The graph is the intelligence layer; the artifacts are how that intelligence becomes actionable for engineering teams.
Rajeev: How do you see AI Architect changing the way development teams manage the software lifecycle—from development and debugging to scaling and long-term maintenance?
Amar: The biggest shift is moving AI from implementation into planning and design. Most teams underestimate how much time goes into the planning phase—engineers spending days reading code across repositories to assess feasibility, mapping data flows and dependency chains manually, stretching planning cycles from days to weeks because context gathering is slow and disconnected.
AI Architect compresses that into minutes. A product manager creates an epic, and AI Architect generates a feasibility analysis: can this be done, what’s the scope, where are the risks? It produces an epic breakdown that maps requirements to specific services, APIs, and modules. It runs spike analyses on the unknowns. All of this is grounded in the actual system architecture through the knowledge graph, not assumptions. The planning artifacts become the handoff point between product intent and engineering execution.
That structural foundation carries through every subsequent stage. Code generation runs faster because agents understand your patterns, APIs, and dependencies before writing the first line. Production debugging becomes traceable through full call chain visibility rather than depending on the one engineer who built that service three years ago.
The compounding effect is what matters for engineering leadership. New developers onboard 50% faster. Code generation runs 5–9x faster. But the real unlock is at the planning layer: engineering teams making better architectural decisions faster, with every agent and every developer working from the same structural understanding of the system.
Rajeev: What role will AI-driven architectural intelligence play in enabling faster, safer, and more reliable software development for enterprises?
Amar: Architectural intelligence will become the foundation that determines how far AI-assisted development can scale in enterprise environments. The gains organizations have captured so far—faster code generation, shorter review cycles, quicker onboarding—all hit a ceiling that model improvements alone can’t raise. That ceiling is structural understanding.
Crossing it requires treating your codebase as a machine-readable intelligence asset. That’s what the knowledge graph does, and the planning artifacts that sit on top of it—feasibility analyses, epic breakdowns, spike analyses, implementation plans—are how that intelligence translates into engineering decisions. The enterprises that build this foundation earliest compound the advantage. Every new service, every refactor, every API contract added to the graph makes every subsequent planning artifact and every agent decision more accurate.
The safety dimension is equally important. Agentic development is moving fast, and the risks of autonomous agents making architecturally incorrect decisions at scale are real. The planning layer is what gives engineering leaders confidence to move from AI-assisted development to AI-driven development. When an agent has a feasibility analysis and an architectural plan before it writes code, the risk profile changes fundamentally. That’s the shift we’re building toward.
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