What
Legacy Modernisation Really Means
Legacy modernisation means taking an important but old system and turning it into a flexible set of smart software helpers that quietly do the same jobs, but faster, smarter and more securely, without disrupting normal operations.
Instead of replacing everything in one risky go, the existing system is gradually converted into a set of software agents that take over specific tasks like data processing, integration, monitoring, and reporting.
People continue working as usual while agents improve automation, insight, and error handling. Over time, this reduces technical debt, improves agility, and modernises technology with confidence while keeping core business processes stable and uninterrupted.
Top Problems Of Having A Legacy Application
Legacy systems create compounding challenges across
your entire organization
Cost and effort
Old technology requires constant patching, specialist skills, and often additional hardware, increasing overall maintenance costs.
Even small enhancements are slow, risky, and expensive due to tightly coupled code and accumulated technical debt.
Performance and scalability
Systems experience slow response times, overnight batch jobs, and increased user frustration.
On-premise or monolithic architectures struggle to handle growth, demand spikes, or the addition of new channels.
Security and compliance
Older platforms lack current patches, encryption standards, and modern security controls.
Legacy systems cannot easily adapt to new regulations, data retention policies, or audit requirements.
Integration and innovation
Limited compatibility with modern APIs, cloud services, analytics platforms, and AI tools slows digital initiatives.
Critical information is locked inside legacy applications and requires manual extraction for reporting.
People and knowledge
Fewer engineers are familiar with legacy technologies, increasing reliance on a small number of experts.
Lack of documentation and lost institutional knowledge make systems hard to understand and risky to modify.
How it Works
We transform legacy apps into agents by decoding the code into business intent, redesigning it as modular agent workflows, and progressively deploying agents while safely retiring the legacy. We do this via 3 stages.
Code to Spec
Tame the beast
Turn messy, mysterious legacy code into clear, business-friendly stories of what the system actually does.
Spec to Design
Reimagine the playbook
Transform those stories into smart, modular agent designs that show who does what, when, and how they collaborate.
Design to Code
Ship the new cast
Build and deploy modern agents that quietly replace legacy behaviours, one slice at a time, without disrupting the business.
Code to Spec
Tame the beast
Decompile complexity into clarity
Map flows, rules, data movements, and hidden dependencies.
Explain it in plain English
“What happens when…” narratives your product, ops, and audit teams can actually use.
Create a living spec
Searchable, versioned documentation that stays aligned with the code as it evolves.
Outcome
A trusted blueprint of current behaviour—ready for modernisation, without guesswork.
Spec to Design
Reimagine the playbook
Define the cast
Agents, responsibilities, tools, and boundaries with clear ownership and minimal overlap.
Design collaboration
Events, hand-offs, decision points, and escalation paths.
Build for governance
Observability, controls, security, and policy-by-design baked in from day one.
Outcome
An agentic target design that’s modular, testable, and business-aligned.
Design to Code
Ship the new cast
Slice-by-slice replacement
Introduce agents alongside the monolith, then progressively take over specific behaviours.
Prove it in production
Automated tests and monitoring to validate parity, performance, and risk.
No big-bang rewrites
Reduce outages and change fatigue while steadily modernising the estate.
Outcome
Working agents in production—measurable value now, continuous migration over time.
Comparing Benefits
Here is a phase wise approximate time and cost savings comparing manual vs our approach
| Dimension / Phase | Manual (typical) | AI/Agent-based | Time saving | Cost saving |
|---|---|---|---|---|
| Discovery & code understanding | 100% effort: manual code reading, SME interviews, documentation recreation. | GenAI agents reverse-engineer code, map dependencies, draft specs; humans review. | 30–50% | 20–40% |
| Business rules & specs extraction | Slow, SME-dependent workshops and manual document writing. | Summarisation and analyst agents generate rule catalogs and specs in natural language. | 40–60% | 30–50% |
| Target design & architecture | Manual design sessions, multiple iterations, limited what-if analysis. | AI-supported design templates; agents propose patterns, humans refine. | 20–40% | 15–30% |
| Code translation / new build | Large dev teams hand-write or port code with high manual effort. | Code-generation and refactoring agents produce 50–70%+ of code; engineers focus on edge cases. | 40–60% | 30–50% |
| Test case creation & execution | Manual test design from specs with limited regression coverage. | AI generates tests from code and logs; agents execute and analyse test suites. | 40–60% | 30–50% |
| Cutover & migration | Big-bang releases with long freeze windows and high operational risk. | Agents wrap legacy systems, enabling phased migration and parallel runs with reduced downtime. | 30–50% | 20–40% |
| Post-go-live optimisation & support | Manual tuning, ticket handling, and periodic performance reviews. | Monitoring agents detect issues, propose fixes, and automate optimisations. | 30–50% | 20–40% |
| Overall programme (end-to-end) | Baseline: 100% of original duration and budget. | Typical 40–50% acceleration; some tasks achieve >100× speed improvements. | 40–50% | 30–40% |
Legacy Agents
Platform Story
From static code to living, goal-driven systems Transform buried logic into intelligent, autonomous operations
The Core Problem with Legacy
Legacy technologies (COBOL, PL/I, JCL, Natural, etc.) share common constraints:
Logic is buried in code
Not expressed as intent
Behaviour is static
Not adaptive
Execution is batch-oriented
Not event-driven
Change requires human intervention
Systems respond to triggers instead of anticipating.
Knowledge lives with scarce experts
Understanding is in people, not systems.
Transformation Model
We transform legacy systems in three deliberate, non-destructive stages.
Code → Spec
Make the invisible visible
WHAT HAPPENS
- ✓Parse legacy code
- ✓Extract rules, flows, constraints
- ✓Produce machine-readable specs (policies, workflows, decision graphs)
Agent Value
Agents now understand what the system does, not just how it runs.
Spec → Design
Reimagine behaviour, not code
WHAT HAPPENS
- ✓Specs become agent blueprints
- ✓Business intent is separated from execution
- ✓Responsibilities are modularised
Agent Types Introduced
Decision agents, Routing agents, Compliance agents, Scheduling agents
Design → Code
Create an agent-ready foundation
WHAT HAPPENS
- ✓Legacy logic runs as APIs & events
- ✓Kubernetes becomes the execution fabric
- ✓Services expose intent + telemetry
Agent Value
Agents can now observe, reason, and act.
Legacy → Agents
Legacy systems become agent-supervised assets, not liabilities.
Agents Take Over
- ✓Agents take over decision making
- ✓Exception handling becomes autonomous
- ✓Optimisation runs continuously
Code Intelligence
Parse, Map, Explain
Code Intelligence
Parse, Map, Explain
Code Intelligence
Parse, Map, Explain
Code Intelligence
Parse, Map, Explain
Code Intelligence
Parse, Map, Explain
Why This Works
Agents introduced incrementally
No big-bang rewrite
Legacy systems continue to run
Business meaning preserved
Human expertise becomes encoded
This is evolution, not replacement.
Why This Works
No big-bang rewrite
Legacy systems continue to run
Business meaning preserved
Human expertise becomes encoded
Transformation Model
We transform legacy systems in three deliberate, non-destructive stages.
Core Programming Languages → Modern Equivalents
| Legacy Technology | Typical Modern Replacement | Notes / Migration Approach |
|---|---|---|
✓COBOL | ✓Java, C#, Kotlin, Spring Boot | ✓Re-platform core logic as domain services; often strangled rather than rewritten |
✓Assembly (HLASM) | ✓C/C++, Rust | ✓Only migrate where unavoidable; often isolate behind APIs |
✓PL/I | ✓Java, C#, Python | ✓Logic extraction + service refactoring |
4GL / Proprietary Languages → Modern Equivalents
| Legacy Technology | Modern Target | Notes |
|---|---|---|
✓SAG Natural | ✓Java + REST APIs | ✓Often part of Adabas → relational migration |
✓CA-Ideal | ✓Java, .NET | ✓Logic extracted into microservices |
✓Cincom Mantis | ✓Low-code (Power Platform), Java | ✓UI + business logic separation |
✓Mark IV | ✓SQL, BI tools (Power BI, Tableau) | ✓Reporting modernisation |
✓APL | ✓Python, R | ✓Mathematical logic translation |
Specialized / Obsolete Languages → Modern Equivalents
| Legacy Technology | Modern Target | Notes |
|---|---|---|
✓JOVIAL | ✓C++, Ada | ✓Defence/aerospace modernisation |
✓SNOBOL | ✓Python, Perl | ✓Text processing rewrite |
✓ALGOL | ✓Java, C++ | ✓Conceptual rewrite required |
✓BASIC | ✓JavaScript, Python | ✓Typically UI or batch rewrite |
Mainframe Control & Support → Cloud-Native Equivalents
| Legacy Technology | Typical Modern Replacement | Notes / Migration Approach |
|---|---|---|
✓JCL | ✓CI/CD pipelines, Workflow engines | ✓GitHub Actions, Azure DevOps, Argo |
✓CLIST | ✓Python, Bash | ✓Script migration |
Batch & Scheduling
| Legacy Pattern | Modern Equivalent | |
|---|---|---|
✓Mainframe batch jobs | ✓Event-driven workflows | ✓ |
✓Nightly batch windows | ✓Streaming / near-real-time | ✓ |
✓File-based integration | ✓APIs / messaging | ✓ |
Proof of Concept Plan
Here is a concise 6‑week proof‑of‑concept plan
Enablement & Scoping
Activities
Run our accelerator over the selected legacy code to extract structure, flows, and business rules; validate findings with SMEs through short read-out sessions.
Outcomes
PoC charter, success criteria, RACI, agreed legacy scope (codebase, interfaces, test data), and non-functional constraints (security, privacy, performance).
Code to Spec
Activities
Run our accelerator over the selected legacy code to extract structure, flows, and business rules; validate findings with SMEs through short read‑out sessions.
Outcomes
PoC charter, success criteria, RACI, agreed legacy scope (codebase, interfaces, test data), and non‑functional constraints (security, privacy, performance).
Spec to Design
Activities
Translate the validated spec into an agentic architecture for the PoC (agent roles, inputs/outputs, contracts, observability); hold design reviews with architecture and security.
Outcomes
Target blueprint for the PoC slice, including agent interaction diagram, data flows, security controls, and alignment notes against enterprise standards.
Design to Agents
Activities
Implement the first set of agents and supporting services; create automated tests from the spec and known edge cases; run iterative test–fix cycles.
Outcomes
Working agent implementations for the PoC scope, passing regression and edge‑case tests, with basic monitoring and logging enabled.
Deploy & Parallel Run
Activities
Deploy agents into a lower or pre‑prod environment; connect to legacy interfaces; run a controlled parallel run using historical and live transactions
Outcomes
Side‑by‑side comparison of agent vs legacy behaviour (accuracy, latency, failure modes), plus a run report highlighting discrepancies and improvements
Learnings & Business Case
Activities
Capture technical, operational, and stakeholder feedback; quantify time and effort saved in discovery, design, build, and testing; document risks and mitigations
Outcomes
PoC report and demo, quantified benefits (cycle‑time and effort reduction for the slice), roadmap options, and a business case for scaling modernization with agents
Technology Partners
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