Many IBM i organizations are excited about AI. The promise is speed, efficiency, and relief for teams stretched thin by developer retirements and mounting backlogs.
What they often get instead is modest improvement. But the real work still moves slowly through the same bottlenecks that have accumulated over decades.
The issue isn’t the AI technology. The issue is how development workflows through organizations built around legacy applications.
Where Development Work Gets Stuck
In organizations running decades-old IBM i applications, critical work exists in places that are hard to access. Business logic lives inside RPG programs and COBOL modules written 20-30 years ago. Rules are embedded in code rather than documented. Progress depends on knowing which veteran developer to ask, which programs are safe to touch, and which interconnections might break.
Much of this knowledge exists only in people’s heads. This person wrote that pricing module. That developer knows why the inventory calculation works that way. We can’t change this without checking with someone who retired three years ago.
As experienced RPG developers approach retirement age, this knowledge becomes increasingly concentrated among fewer people. Many organizations find themselves with only a handful of developers who understand decades-old business logic. A typical IBM i application portfolio includes millions of lines of code, with undocumented dependencies between programs, databases, and business processes that only veteran developers understand.
When a single error in a nightly batch RPG program can prevent orders from shipping or invoices from generating, organizations naturally become conservative about changes. This risk aversion creates layers of protection: multi-step approval processes, careful change documentation, extensive testing protocols, and special access restrictions. Those layers made sense when green-screen interfaces offered no guardrails and testing required manual 5250 sessions.
But they’ve also made it nearly impossible for AI coding assistants to deliver meaningful value.
Why Traditional AI Coding Tools Can't Break Through
Most AI coding assistants are designed for modern development environments: single repositories, cloud-based CI/CD, comprehensive test suites, and developer teams comfortable with rapid iteration. They assume code changes can be quickly validated and rolled back, testing is automated, documentation is current, and dependencies are explicit.
IBM i environments rarely match these assumptions. Traditional AI coding tools struggle because they:
- Can’t Complete Work Autonomously: Coding assistants suggest and can make changes, but developers still need to compile, test, identify issues, prompt the AI again, and repeat. The organizational overhead of managing each iteration remains unchanged. Every suggestion still requires navigating multi-step approval processes, coordinating with stakeholders, and manually verifying that changes don’t break undocumented dependencies.
- Lack IBM i System Access: Most AI coding tools can’t directly interact with IBM i environments, DB2 databases, and RPG/COBOL compilers. Organizations must extract code and context, introducing new bottlenecks.
- Don’t Eliminate Risk Controls: When AI tools only assist rather than finish tasks, organizations still need the same approval processes, testing protocols, and risk controls, because work is still incomplete and unverified.
The result? AI coding assistants promise lofty productivity gains but deliver single-digit improvements once organizational friction is factored in.
How Agentic Coding Eliminates the Bottleneck
CoderFlow represents a fundamentally different approach. Rather than assisting developers through each step, it autonomously completes development work and presents verified outcomes for approval.
This shift from assistance to autonomous completion changes what’s organizationally possible:
Operates Within Your Infrastructure
CoderFlow runs on-premise or in your private cloud. All execution, builds, tests, and Git operations stay inside your network. Only minimal code context is sent to cloud LLMs; no full repositories, credentials, or data leave your environment. This security model eliminates the lengthy approval processes required for cloud-based tools; you can deploy agentic coding without restructuring security governance.
Multi-Repository and Direct IBM i Integration
IBM i environments aren’t single-repo greenfield projects. CoderFlow orchestrates work across multiple repositories and systems, including direct IBM i integration for RPG, COBOL, and 5250 environments. This eliminates the complexity of extracting code, managing context across systems, and coordinating changes.
Parallel Exploration With Automated Evaluation
CoderFlow can run multiple agents simultaneously, exploring different approaches to the same problem. Automated evaluation compares outputs, explains tradeoffs, and selects validated results. Instead of convening meetings to debate architectural decisions, agents explore alternatives and developers review concrete, working implementations with clear tradeoffs documented.
Built-In Governance
Every autonomous action includes complete audit trails, role-based access controls, and developer approval gates. The approval processes, change documentation, and audit requirements that currently slow development become integrated into the agentic workflow rather than external bottlenecks.
Achieving Exponential Productivity Gains
CoderFlow delivers productivity gains by eliminating both technical and organizational friction:
- Parallel Execution: While traditional development is sequential, one developer working on one program at a time; agentic coding enables parallel execution across your backlog. Five agents can simultaneously work on five different tasks, each completing full build-test-fix cycles autonomously.
- Reduced Coordination Overhead: Much of the complexity in IBM i development stems from coordination: checking with others about dependencies, explaining context, waiting for answers. When agents have access to code repositories, documentation, and systems, they handle routine coordination autonomously.
- Developer Role Transformation: Developers shift from writing and debugging code to orchestrating objectives and reviewing verified outcomes. This is powerful in IBM i environments facing developer shortages; fewer developers can accomplish more because they’re not spending time on manual coding and testing.
CoderFlow emphasizes concrete validation over generated text. Agents evaluate their own output using successful builds, passing tests, and expected runtime behavior; objective signals that align with how organizations already define “done.”
Why IBM i Organizations Are Uniquely Positioned to Benefit
While the broader industry struggles with failed AI initiatives, IBM i organizations have an unexpected advantage: decades of proven business logic that just needs to be liberated from organizational complexity.
The challenge isn’t creating new systems from scratch. It’s futurizing existing applications while preserving institutional knowledge and business logic that competitors can’t replicate.
Organizations that successfully adopt agentic coding start with concrete objectives where autonomous completion delivers clear value:
- Documentation Generation: Point agents at undocumented RPG programs and generate comprehensive documentation, eliminating a major source of friction when developers need to understand unfamiliar code.
- Test Creation: Have agents analyze business logic and create automated test suites – establishing validation frameworks that enable confident changes going forward.
- Technical Debt Reduction: Deploy agents to refactor problematic code sections, fix deprecated dependencies, or modernize patterns, work that’s organizationally important but difficult to prioritize in manual development.
Each success reduces organizational complexity. As teams gain confidence that agents produce verified, working code, approval processes become more efficient. As automated testing increases, risk-averse cultures become more comfortable with change. As documentation improves, organizational knowledge becomes accessible.
The Real Change Is Capability Expansion
Using agentic coding effectively doesn’t mean abandoning organizational controls. It means expanding what’s organizationally possible by removing the bottleneck of manual coding and iteration.
Organizations that succeed combine purpose-built technology with organizational adaptation. They reduce unnecessary approval steps while maintaining meaningful controls. They make institutional knowledge explicit, so agents can operate with context. They define “working code” in concrete terms that enable autonomous validation.
In those environments, agentic coding becomes a capability multiplier. Development teams can tackle backlogs that were previously impossible. Organizations can futurize applications before veteran developers retire. Businesses can respond to market changes with the agility of startups while maintaining the reliability of established enterprises.
This feels less like automation and more like having a development team that never forgets context, always documents their work, and reliably delivers verified outcomes, operating within your infrastructure, under your governance, and aligned with your standards.
The Bottom Line
AI struggles in IBM i organizations not because the technology is immature, but because traditional AI coding tools operate as assistants rather than autonomous agents, leaving organizational complexity unchanged.
The IBM i organizations that extract real value from AI won’t be those chasing generic coding assistants. They’ll be the ones deploying agentic coding platforms like CoderFlow that deliver productivity gains by autonomously completing work within enterprise-grade security and governance frameworks.
The future isn’t about working faster within existing constraints. It’s about eliminating the constraints that decades of legacy applications created—while preserving the business logic and institutional knowledge that make your organization competitive.
Ready to see how agentic coding can transform your IBM i development organization?
Learn more about CoderFlow or reach out to our team at Futurization@ProfoundLogic.com to discuss how autonomous agents can accelerate your futurization initiatives.