For IBM i IT managers, backlog isn’t just a list of tasks. It’s a force that shapes everything you do: what gets prioritized, what gets delayed, and how much runway you have for futurization.
You’ve already tried squeezing more out of the same capacity — better processes, smarter prioritization, even AI-assisted coding inside the IDE. Those tools help. But they don’t flip the equation.
Agentic coding does. Not by making developers type faster, but by letting AI complete entire engineering tasks end-to-end — safely, in parallel, and with self-validation.
Understanding the Reality Your Development Team is Facing
Most IBM i teams are navigating the same collision of pressures:
- Backlogs growing faster than teams can burn down
- Retiring RPG/COBOL talent and limited replacements
- High technical debt that makes every change riskier
- Business urgency for modern features and integrations
- Interrupt-driven work (support, incidents, compliance asks) constantly reshuffling priorities
Even when teams execute well, it becomes a loop:
- Handle the highest-risk fires
- Ship incremental fixes
- Watch backlog refill faster than it empties
- Defer futurization because there’s never a safe window
If that sounds familiar, it’s because the math is working against you — not your team.
Why AI-Assisted Coding Isn’t Enough (even when it helps)
Let’s call it what it is: IBM i teams aren’t struggling because they lack skill or discipline. They’re struggling because the demand curve has changed — and the capacity curve hasn’t. Every year, the business expects faster delivery, more integration, and more modernization, while the systems you’re supporting keep getting more complex and the people who know them best are harder to replace.
That creates a kind of quiet pressure that doesn’t show up in a single sprint report. It shows up over time — in the backlog that never gets smaller, the refactors that never quite make the cut, and the growing gap between what the business wants and what the team can safely ship.
IDE tools like Copilot or Project Bob can speed up writing code. But they don’t finish the work.
After the suggestions, your team still has to:
- Compile
- Run tests
- Fix broken logic
- Validate outputs
- Re-test
- Prepare release artifacts
- Support production rollout
The gain feels like:
- Slightly faster typing
- The same validation workload
- The same risk profile
- The same backlog trendline
A 20–25% improvement can make a sprint smoother. But it rarely changes the strategic trajectory of a shop that’s already underwater.
To reverse backlog momentum, you need a gain big enough to outrun it.
The Hidden Opportunity in a 5–10x Productivity Shift
This is where agentic coding becomes a paradigm shift, not a feature.
Instead of AI suggesting code inside a developer’s IDE, autonomous agents execute full development cycles in isolated, containerized environments:
- They take a task
- Write the solution
- Compile it
- Run automated tests
- Validate functionality
- Iterate until it passes
- Return a production-ready result + validation report
Even more importantly: agents run in parallel across multiple containers.
So, you’re not getting “one developer a bit faster.” You’re getting “multiple autonomous workers finishing tasks end-to-end while your team reviews outcomes.”
That’s why the productivity jump is nonlinear:
- AI assistants help your team row faster
- Agentic coding adds more rowers, who can work overnight and in parallel
What tasks are ideal for autonomous AI agents?
Not every task should be handed to an autonomous agent on day one and that’s a good thing. The goal isn’t to “AI everything.” The goal is to offload the right categories of work first: the ones that are repeatable, time-consuming, and easy to validate through automated testing.
Think about the backlog items your team could knock out quickly if you had more hours, or more hands, without increasing risk. Those are your first wins. And in IBM i environments, that list is usually longer than people expect.
Agentic coding thrives on exactly the categories clogging IBM i backlogs:
Refactoring at scale
The backlog items you want to do but never have time for:
- Free-format conversions
- Modularization
- Removing dead logic
- Shifting RLA to SQL
- Standardizing patterns for maintainability
Agents can refactor, compile, test for equivalence, and show exactly what changed.
Test hardening and automation
If your backlog includes “we should add tests here” tasks:
- Screen-flow recording + replay for 5250/RDF
- Regression test scaffolding
- Unit test expansion
- Data validation checks
Agents can expand coverage systematically, then validate automatically.
Conversion and cleanup work
Especially during or after transformation:
- Converting RPG/COBOL modules to modern languages
- Optimizing converted code further
- Creating RESTful services
- Incremental UI modernization
- Backlog sweeps across repeatable ticket patterns
This is where your hybrid approach matters: deterministic algorithms handle safe bulk conversion, AI handles the hard edges, and agents validate everything.
Governance: Agents Speed Delivery, Humans Keep Control
Whenever AI enters the development process, the first question from a responsible IT manager is the right one: “How do we keep this safe?” Because speed without control isn’t improvement — it’s just a faster path to production risk.
Agentic coding is built for that reality. It doesn’t ask you to trust a black box or loosen standards. It gives you a new capacity model inside the same guardrails you already rely on — with even more validation than most human workflows can realistically sustain.
Your guardrails stay intact:
- Agents work in isolated containers
- Changes can’t go to production without approval
- Compilation/testing is required before submission
- Agents provide validation evidence for review
- Your team still owns final PR / release decisions
You get the throughput lift without surrendering risk management.
What 5–10x Productivity Actually Changes for IT
A 5–10x productivity shift isn’t “we finish tickets faster.” It’s a reset of what your team is capable of delivering , and how you run IT.
As our CEO Alex Roytman puts it, agentic coding represents a major industry shift because it doesn’t just accelerate coding, it changes the entire development equation. In the past, transformation conversations were mostly about moving from a legacy language to a modern one and waiting years to realize the benefits. Today, customers are demanding faster results, clearer ROI, and more flexibility in what they futurize and where they go next. That kind of delivery would have been difficult under a purely manual or AI-assisted model. With agentic coding, it becomes achievable.
And that’s not theoretical. We’ve seen it firsthand in our own product work. When a customer asked for a proof of concept converting 1,000,000 lines of COBOL, we were able to deliver syntactically correct JavaScript in roughly a month, and then rapidly validate that conversion by hardening the testing foundation around it. What made this possible wasn’t “AI helping someone type.” It was autonomous agents taking on full execution workstreams end-to-end: generating conversions, building out test coverage, iterating on failed cases, and returning results with evidence.
In Alex’s own hands-on experience, work that would normally take weeks or months of serialized backlog effort can collapse into hours when agents are able to execute, test, and iterate autonomously.
Alex also emphasizes that this shift changes the developer’s role in a healthy way. The highest-value work becomes understanding the problem, decomposing it, and architecting the solution, then directing and validating agents as they execute. When that loop is in place, productivity doesn’t rise linearly. It jumps, because you’re no longer constrained by what humans can hand-code, hand-test, and hand-iterate in sequence.
Here’s what that kind of capacity change actually means for IT teams:
- Backlog stops feeling inevitable. Instead of triaging forever, you start retiring categories of work. The list shrinks in visible chunks, not inches.
- Refactoring becomes routine, not a luxury. The “we’ll clean that up later” pile finally gets attention because agents can chew through systematic improvements while your team stays focused on priority delivery.
- Your best people stop living in maintenance mode. Agents handle repeatable execution work; humans move up-stack into architecture, standards, futurization strategy, and the hard problems only experienced engineers should be solving.
- Time-to-market changes shape. Features that used to take months because they competed with everything else start landing in weeks — not because corners are cut, but because capacity is no longer the limiting factor.
- You scale output without scaling headcount. Instead of relying on hires you can’t find, you add parallel agent capacity you can run, safely and continuously.
- Quality improves with velocity. Agents don’t skip validation when they’re busy. They compile, test, replay, and document proof every time, reducing defects while throughput climbs.
The practical result is simple: you move from backlog survival to delivery leadership. Not someday, and not after a multi-year rewrite — but as a measurable, defensible shift in capacity your business can feel quarter by quarter.
The Path Forward: Prove it Safely, Then Scale
Phase 1
Discovery (clarity before change)
Before any conversion or agents touch your backlog, we map the landscape:
- Application inventory and dependency flows
- Business-critical logic zones vs. safe pilot zones
- Where technical debt is slowing delivery most
- What testing coverage exists today and where it needs reinforcement
Outcome: A prioritized, low-risk transformation and agentic adoption plan tied to real backlog pain.
Phase 2
Targeted POC (prove equivalence, not just output)
We pick a non-critical but representative module, something real enough to matter, safe enough to pilot, and run it end-to-end:
- Algorithmic + AI refactoring for predictable conversion
- Automated testing for functional equivalence
- Agentic task execution in containers
- Validation reports your team can review line-by-line
Outcome: Transformation that improves delivery without asking the business to pause.
Phase 3
Incremental Rollout with Coexistence (no downtime, no big-bang risk)
As confidence builds, we expand in controlled slices:
- Futurized components run side-by-side with legacy
- Changes roll out behind clear guardrails
- Business continuity stays intact
- Each milestone delivers measurable value, not just progress
Outcome: Transformation that improves delivery without asking the business to pause.
Phase 4
Agentic-Native Delivery (capacity becomes programmable)
Once the foundation is in place, agents move from “pilot” to “daily force multiplier”:
- Parallel containers for multiple autonomous tasks
- Compilation + testing required before submission
- PR-style approvals stay with your team
- Backlog categories get assigned to agents systematically
Outcome: Your throughput scales elastically, overnight, in parallel, without proportional headcount growth.
The key point: every phase produces value on its own. You’re never waiting for a multi-year finish line to see ROI. You’re proving safety, recovering capacity, and accelerating delivery step-by-step with coexistence ensuring the business never takes a hit.
Your Next Step: Turn Capacity Pressure into a Proven Breakthrough
What you’ve seen here isn’t just a new tool category; it’s a practical framework for escaping backlog gravity without sacrificing safety. The difference between incremental improvement and real transformation comes down to one question: are you still asking your team to do more with less, or are you expanding capacity in a controlled, measurable way?
Ready to see what agentic tasks look like in your IBM i environment? Reach out to our team at Futurization@ProfoundLogic.com to explore a targeted Proof of Concept.
The IBM i organizations that will lead the next decade won’t be the ones chasing risky rewrites or hoping for marginal AI boosts. They’ll be the teams that futurize incrementally, protect business logic through coexistence, and unlock 5–10x development capacity with autonomous agents working safely in parallel.
Your backlog doesn’t have to define your roadmap. With the right guardrails and the right partner, it becomes the proof point that change is working. The time to reclaim capacity is now.