Stop Fixing Legacy Java—Let the AI Do ItStill hand-upgrading legacy Java? That’s not craftsmanship; that’s unpaid penance. Manual modernization is a failure mode—slow, error-prone, and permanently behind. The truth? AI agents now handle the drudgery with receipts.Here’s what you actually get: time back, CVEs gone, and cloud bills that stop bleeding. We’ll walk a narrative case study—stack, benchmarks, results—plus a full audit trail so governance can breathe. There’s one metric that embarrassed finance and delighted security—stay for that. Now, here’s what happens when you let Copilot App Modernization drive.Case Setup: The Legacy Stack and the Modernization MandateBaseline first. We’re dealing with a Java 8-era Spring application—classic MVC plus a sprinkle of scheduled jobs—built with Maven, running on AWS. Conservative governance, noisy alerts, everyone swears the app is “stable” while PagerDuty begs to differ. The stack has drift: parent POMs forked years ago, dependency versions pinned like insect specimens, and a CI pipeline that only passes if you chant. Average user behavior? Ignore the red badges, silence the scanner, and pray the next sprint includes “tech debt.” Spoiler: it never does.Pain inventory. Version drift means modern libraries won’t resolve without exclusions. Dependency hell: transitive roulette where one logging upgrade detonates your HTTP client. Unpatched CVEs sit there, politely waiting for an exploit kit. Idle compute waste? Autoscaling that never scales down, instances at 8% CPU while finance funds a space heater. Add brittle configs: environment variables baked into user data, stateful file writes on disk, and secrets living where secrets shouldn’t live.Constraint: this is audio-only. No performative heroics, no live tab-switching to Stack Overflow. We rely on reproducible artifacts—reports, commit diffs, scanner outputs. Because guesswork is not a strategy, and “worked on my laptop” isn’t evidence.Why Java 21. Virtual threads change concurrency from a scarce resource to a commodity. Thousands of lightweight threads with minimal overhead; throughput goes up, tail latency comes down. Garbage collection improvements—G1 and ZGC refinements—reduce pause times. The Foreign Function and Memory API is stable, which matters if you’re calling native code or wrestling with off-heap buffers. Net effect: measurable performance and maintainability gains. Most people think upgrading is cosmetic. Incorrect. You get cheaper concurrency and fewer stalls—directly visible in service-level graphs.Migration scope: AWS out, Azure in. Align with enterprise standards, consolidate billing, and plug into the platform your identity, policy, and observability already inhabit. We’ll target Azure App Service or Azure Spring Apps depending on the workload profile—simple app, App Service is fine; Spring-heavy microservices with scaling nuance, consider Azure Spring Apps. For data, the mandate is Azure SQL Database; yes, you can keep PostgreSQL on Azure, but the business wants consolidation, so we’ll map the path and show the trade-offs.Governance stance: every action lands in Git. Diffable, reversible, attributable. The agent generates a plan, opens issues, and proposes commits. You approve. It resolves builds, patches dependencies, applies code transformations using known recipes, regenerates the SBOM, and reruns scanners. If something doesn’t pass, it loops—no alt-tab pilgrimage. And when it can’t proceed, it asks for input and documents why. See the difference? Work becomes auditable instead of anecdotal.Before you let an agent touch anything, you assess. Automated inventory across modules, dependencies, build plugins, and runtime configs. Risk-ranked findings with references to advisories and documentation. Cloud readiness flags for service bindings, environment variables, and stateful traps that break when containers come and go. Cost baselines: compute hours, idle percentages, and the egress patterns that finance pretends not to notice until month-end.You might be thinking, “We can triage that by hand.” Fascinating. And you’re still surprised the app crashes. The average user misses the long-tail issues—deprecated APIs deep in the scheduler, a logging bridge masking duplicate class conflicts, or that one library pin that blocks everything from moving. The agent doesn’t miss them because it doesn’t get bored.So the modernization mandate is simple and final: upgrade to Java 21, eliminate CVEs, containerize, migrate to Azure, wire CI/CD with controlled rollouts, enforce policy via approvals and Key Vault, and cut cost without cutting reliability. All changes trace back to commits with rationale. No black box. No swaggering hero-commits at 2 a.m.Assessment first—because guesswork is not a strategy. Then we execute.Assessment: The AI Exposes Technical Debt with ReceiptsHere’s what actually happens when you press assess. The agent doesn’t “scan.” It inventories—code, build files, plugins, transitive dependencies, Docker bits, environment variables, startup scripts, even those stray shell wrappers someone copy-pasted in 2017. It builds a dependency graph, annotates it with CVE data, and ranks risk. Not vibes—severity, exploitability, reachability. It’s the Windows Registry of your app’s reality. Not just a list—your structural spine, exposed.The truth? Most people think they know their stack. They don’t. The agent finds the forked parent POM with a logging version pin that blocks every downstream patch. It flags duplicated SLF4J bridges that shadow each other like feuding roommates. It catches the servlet container that’s quietly three releases behind because your CI only tests the happy path. And yes, it maps deprecated APIs you’ve been calling for years because nobody wanted to touch the job scheduler. Spoiler alert: you’re touching it now.CVEs next. Severity breakdown, affected libraries, and references to advisories—direct links, not rumors. Critical and high get top billing, but it also identifies whether the vulnerable code paths are reachable by your application. Reachability matters. If a transitive library has an issue but your code never calls the vulnerable class, it’s still flagged, but the agent prioritizes fixes that reduce real risk first. You get the remediation options: bump versions, add exclusions, or swap artifacts entirely. That’s a menu, not a mystery.Upgrade readiness comes with receipts. Java 21 requires you to stop pretending it’s 2009. The agent runs OpenRewrite recipes against your codebase in dry-run mode, showing exactly which APIs are deprecated, which imports must change, and where behavior shifts lurk. It calls out illegal reflective access, j.u.c. quirks, and the tiny landmines waiting in XML configuration. It notes frameworks that are ready—Spring versions, plugin compatibility—and those that need coercion. No hand-waving. Every proposed change links back to docs and migration notes.Cloud readiness is where your infrastructure sins go to be cataloged. The agent identifies stateful traps: writing temp files to local disk during requests, caching sessions in memory across instances, storing secrets in a text file under /opt because someone “needed it to work.” It flags environment variable usage that assumes EC2 metadata patterns. It points out service bindings for Redis, queues, and databases that won’t survive container churn. Then it proposes bindings the Azure way: connection strings moved to configuration, Key Vault for secrets, managed identities for auth. You know, like adults.Now the finance shocker: cost baselines. The agent pulls compute hours, instance sizes, scale patterns, and idle percentages from telemetry and infra definitions. It estimates egress costs based on outbound patterns—yes, that one noisy batch job that hurls data across regions every night gets a line item. It translates all of this into a monthly number that doesn’t care about your anecdotes. This is the number that embarrassed finance and delighted security. Why? Because eliminating CVEs while cutting spend is the only religion both teams share.Artifacts or it didn’t happen. The agent produces a plan file with sections: CVE remediation steps, Java upgrade recipes, build changes, code transformations, containerization moves, and cloud target mappings. It opens issues per workstream, tags owners if you want, and scaffolds commits in a separate branch—clean, small diffs with rationales in the messages. SBOM generated, signed, and versioned. Vulnerability scanner outputs attached. That’s audit-ready from step zero.You want traceability? Every finding links to an advisory or a source document: NVD entries, project release notes, OpenRewrite recipe documentation, Azure migration guides. When it suggests replacing a library, it cites compatibility matrices. When it recommends moving a secret, it points to platform guidance on Key Vault and managed identities. It’s not just “because AI said so.” It’s “because this standard, right here, says so.”Controls remain human. The plan sits in Git, awaiting approval. You can adjust priorities, reject a remediation strategy, or demand a different target service—Azure App Service versus Azure Spring Apps—with the trade-offs listed plainly. The agent adapts, reruns the assessment delta, and updates artifacts. No sulking.Once you see the receipts, the posture changes. You stop debating if modernization is “worth it” and start sequencing the work. And yes, the average user will still try to ship a feature first. Fascinating. Meanwhile, the agent has already mapped the shortest path to a secure, compliant, cheaper runtime. Approve the plan. Then watch it do the work you keep postponing.Automated Upgrade: From Java 8 to Java 21 Without the DramaPlan approved, so the agent stops talking and starts doing. The loop is simpl
