The Decision
A tech risk backlog growing faster than manual remediation could clear it
The client is a global investment group, and the engagement sits within the middle office of its asset and wealth management business, which runs on a large estate of long-lived applications. Technology Risk Office scans run continuously across that estate and identify end-of-life software, unpatched CVEs, framework drift and insecure protocols still carrying production traffic, and the working backlog they produced held more than 5,000 tech risk items.
In a regulated institution, end-of-life software and open CVEs carry audit and compliance consequences well beyond the technology budget. The client asked Apexon for a ten-member team to remediate these items manually, a model planned to deliver 1,000 work items at roughly two to three library upgrades per engineer per day.
The Problem, Reframed
The manual model would have addressed a fraction of the backlog while scans continued to add to it
A manual team adds capacity without changing the underlying rate of remediation, and at the planned pace the team would address 1,000 items against a backlog of more than 5,000 while Technology Risk Office scans continued to surface new findings.
The nature of the work pointed to a better option, since end-of-life library replacement, Java and Spring version upgrades, operating system and scheduler migration, and insecure protocol remediation each follow a consistent pattern from one instance to the next, yet every item was being handled as a separate task, with the knowledge of how to fix it correctly held by individual engineers. Tech risk work of this kind is outside the core focus of application development teams, so it had little prospect of being absorbed into regular delivery.
Encoding that recurring knowledge once, in a form AI agents could apply consistently under engineering governance, offered a way to change the rate of remediation itself, so Apexon proactively proposed an agentic solution in place of the manual model.
The Work
Apexon set up the harness in the first six weeks and scaled delivery through it
The first six weeks of the program went to setup, during which a seed team led by an architect built the agents and the delivery team was upskilled on agentic engineering. Once that setup was complete, agents moved into delivery and began accelerating output from the same team.
The engagement was delivered as Harness Engineering, enabled by AgentRise Harness™, on an agentic development environment connected to the client’s data sources and governed by a dedicated security layer, with two components of the harness carrying the delivery model:
- Golden Paths. Reusable technical blueprints that encode the coding standards and guardrails for each remediation type, so that a recurring fix is defined once as a governed playbook and applied across every instance of that type.
- Agentic Pods. Delivery units in which specialist AI agents execute those playbooks in batches, with human validation gates at design, development, test and deployment, and an engineer approving every change before it merges.
Quality engineering ran inside the delivery cycle, with AI-enabled self-healing test frameworks keeping validation from becoming a constraint once remediation moved to batch processing. Delivery was coordinated closely with the client’s application development teams throughout the engagement.
Scope covered:
- End-of-life software libraries
- End-of-life Spring libraries, including Java and Spring version upgrades
- RHEL 7 end-of-life
- AutoSys migration to a modern scheduler
- Insecure protocol remediation across FTP, HTTP and Kafka
- Legacy resource decommissioning
What Changed
The same ten-person team delivered 2.4 times its planned volume
Once the harness was running, output rose several times over with no change to the size of the team, which remained at ten people throughout. Against the 1,000 items planned for the team without agents, it has delivered 2,414 across six workstreams.
On library upgrades, each engineer now completes twelve to fifteen items a day compared with two to three under the manual model, which is roughly five times the previous rate. Effort per remediated item is 60% lower than the manual baseline, and the 1,414 items delivered above plan are attributable to agent throughput.
The client asked for capacity sized for manual work and now has a team of the same size delivering well beyond that plan, with audit posture and compliance readiness improving in parallel. The program has delivered validated annual savings of $0.20M, and contractor spend is set to fall by approximately 40% after Year 1. The Golden Paths remain in place after the work that produced them, so new scan findings of the same types move through the same governed process, and the framework extends to future risk scans, L3 support, release and deployment management, new integrations and end-of-life decommissioning at no additional spend.
Outcomes
2,414
Items delivered across six workstreams, 2.4x the 1,000 planned without agents
~5x
Per-engineer throughput on library upgrades, from 2–3 to 12–15 items a day
60%
Lower effort per remediated item than the manual model
$0.20M
Validated annual savings
Supporting the business story
Same ten-person team throughout, with no additional headcount
1,414 items delivered above baseline through agent throughput
Agents in delivery six weeks after program start
~40% reduction in contractor spend after Year 1
Improved audit posture and compliance readiness
Framework extends to future risk scans, L3 support, release and deployment management, new integrations and end-of-life decommissioning at no additional spend
Close coordination with the client’s application development teams
What Did Not Get Worse
Every change that merged was reviewed and approved by an engineer, and per-engineer throughput on library upgrades rose roughly fivefold with no findings suppressed and no change to the standards the estate is audited against.
Why These Choices, Not the Obvious Ones
Staff to the manual plan
Why encode the recurring fixes instead of staffing against the backlog?
Why the obvious path would have failed. A manual team adds throughput at a cost that rises with the backlog, and because scans continue to surface new findings, the backlog would return to its previous level once the engagement ended and the additional capacity left.
The insight behind the choice. The backlog came down to a limited set of recurring patterns executed many times over, so encoding each pattern once as a governed playbook converted a cost that scales with volume into a reusable capability that allowed the same ten people to deliver 2.4 times the planned volume.
AI assistants per engineer
Why build a governed AI SDLC instead of handing every engineer a coding assistant?
Why the obvious path would have failed. Assistant-level tooling improves individual productivity in a linear way, with context lost between sessions and standards left to individual judgment, and in a regulated estate a fast change without consistent evidence is harder to defend at audit than a slower change with a complete record.
The insight behind the choice. The constraint was repeatability under governance, meaning the ability to apply the same validated fix across thousands of instances and evidence it afterward, and that capability resides in the framework that governs the agents.
Point automation per remediation type
Why build a single governed framework when six workstreams were in scope?
Why the obvious path would have failed. Automating each remediation type separately clears that type while the next one starts from the beginning, and the estate ends up carrying separate toolchains and maintenance commitments as each new scan surfaces work none of them cover.
The insight behind the choice. Because scanning is continuous, remediation needed to be built as a horizontal capability, and designing for scope that had not yet been defined allows the same framework to absorb L3 support, release management and decommissioning, along with other business groups, without a second engagement.
The Broader Implication
Most regulated estates automated detection years ago, and remediation is where the next operating gain now sits
Tech risk is present in every application estate yet rarely sits at the center of the development teams that own those applications, which allows backlogs to grow faster than they are cleared. The knowledge required to remediate at scale already exists inside most estates, held in the coding standards and conventions engineers apply by hand.
Organizations that classify what their remediation queue contains and encode that knowledge in a form an agent can execute and an auditor can inspect, put in place a capability that continues to deliver with each new scan. For an intelligent enterprise, that capability keeps a regulated estate current and audit ready as its applications continue to evolve.
If tech risk in your estate is accumulating faster than your teams can address it, Apexon can review what your remediation queue contains and define where the first delivery waypoint for a harness in your environment should sit.
FAQ
Automated code remediation uses AI agents to apply fixes to source code at scale, covering dependency upgrades, framework version migrations, security patches and end-of-life library replacement. In an enterprise setting it runs against encoded playbooks that hold the organization’s coding standards and integration patterns, with human validation before any change merges.
Technical debt remediation scales when recurring fixes are encoded once and applied across every instance, since most enterprise backlogs consist of a limited set of patterns repeated many times. Encoding each pattern as a governed playbook converts remediation from a cost that rises with the backlog into a capability that improves over time.
Yes, provided governance is built into the delivery framework from the start, because in banking and financial services every change needs to be traceable to an enforced standard and approved by an engineer before it reaches production. AgentRise Harness™ places validation gates at design, development, test and deployment to provide that evidence.
An AI SDLC is a software development lifecycle in which AI performs most of the iteration and engineers govern at defined gates. It differs from assistant-level adoption, where AI supports an individual engineer and productivity gains stay linear, because its returns build on encoded standards and reusable blueprints that delivery units apply to produce production-ready output.
In this engagement, agents moved into delivery after a six-week setup in which a seed team led by an architect built the agents and the delivery team was upskilled on agentic engineering.
Remediation and modernization address the same estate from different ends, with modernization rebuilding or re-platforming an application and remediation keeping the existing estate current and compliant. For most institutions remediation is the larger and more continuous obligation because scanning keeps surfacing new work, and it determines whether a modernization program starts from a stable base.
