Capability · Agentic legacy migration
Modernise without disruption.
Every organisation runs on systems it can't switch off. We model the move before anything changes, then put teams of AI agents to work reading legacy code, capturing what it really does, and rebuilding it on a modern platform, one service at a time, while the old one keeps running.
At a glance
- For
- Agencies and regulated organisations whose critical services still run on ageing systems
- What you get
- A transition model, an agentic migration factory, and legacy systems decommissioned in phases
- Platforms
- Holocentric Connect, and agents on Azure AI Foundry, AWS Bedrock, Google Vertex AI or GovAI
- Time to value
- A pilot application rebuilt in about 6 to 10 weeks, after a 4 to 6 week discovery
The hardest systems to replace are the ones that matter most. They collect revenue, pay entitlements and hold the record. They can't stop for a migration weekend, and their business rules often live only in the code, written by people who left years ago.
Like-for-like replacements and single big-bang cutovers are where large programs go wrong. We take a different path: understand the whole operating model first, then move one capability at a time, with AI agents doing the heavy reading and rebuilding, and people deciding what ships.
Why it matters
The systems you can't switch off.
Too critical to stop
Revenue, payments and records have to keep flowing every day of the transition. Old and new have to run side by side, safely.
Knowledge locked in code
Years of policy changes are buried in legacy logic. Nobody can say exactly what the system does until someone reads all of it.
Big bangs go bang
Replacing everything at once, through one integrator, concentrates risk at a single cutover date. Phased change spreads it out.
Every change gets slower
New policies, levies and concessions wait on fragile code. Technical debt turns each change into a project.
Process modelling isn't the outcome. It's the control mechanism that lets you change safely.
Model the move
Know what changes before anything does.
On Holocentric Connect we build one authoritative view of how products, services, processes, policies, risks and systems connect, then use it to plan and govern every step of the transition.
Products and services, end to end
Each product family mapped from policy intent through customer journeys and operational processes to the platforms that support it.
Current, interim and target states
Transition states modelled explicitly, so legacy and new solutions can coexist with clear dependencies and sequencing.
Composability tested early
Capability and process boundaries defined before technology choices are locked in, so each component has a clear job.
Impact before decisions
See how a policy, legislative or technology change affects products, processes, systems, roles and controls before it is approved.
Our PACE-D approach
Planning
A shared view of today's operating model, products, services and platforms, with constraints and change-sensitive areas identified.
Analysis
Current and target states compared for impact across products, processes, systems, roles and governance.
Concept
Future scenarios modelled and tested for feasibility, risk and alignment with standards.
Elevate
The agreed model, governance and process standards embedded in day-to-day operations.
Delivery
Transition models, dependency maps and governance artefacts that let change land incrementally.
Modelled on Holocentric Connect. See our Holocentric capability (opens in a new window)
Migrate with agents
AI agents that read the old system and build the new one.
A team of specialised agents analyses existing applications, whatever they were written in, and captures their behaviour in a meta-model. That meta-model becomes the blueprint for generating the same behaviour on the target platform.
Assemble
Code, documents, data and business requirements gathered and prepared for the agents.
Analyse
Specialised agents read each legacy code base and map its structure, data flows and integrations.
Meta-model
Behaviour captured as a technology-neutral model, enriched with business rules from documentation.
Generate
Services, APIs and interface scaffolding generated for the target platform, aligned to your architecture.
Review
People review every change against quality gates for coding standards, security and performance.
Deploy
Phased release with blue-green or canary cutovers, clear rollback, and legacy components retired.
- Many technologies in, one standard out. The same pattern consolidates several legacy code bases into one modern, maintainable one.
- Behaviour preserved, debt left behind. Business logic carries across; obsolete components and workarounds don't.
- People decide what ships. Agents propose; your team reviews, approves and merges.
From pilot to factory
Prove it once. Then run it in parallel.
We start with one application to validate the approach end to end, then turn what worked into a reusable factory.
- 4–6 weeks
Discovery and strategy
Portfolio assessment, a prioritised backlog, the AI project set up in your tenancy, and the target landing zone designed.
- 6–10 weeks
Pilot migration
One application rebuilt by agents and deployed to a non-production environment, with architecture patterns validated and runbooks updated.
- Your decision
Stage gate
Lessons learnt and the approach refined before any further migration is approved.
- Ongoing
Migration factory
Shared patterns, prompts, tools and evaluation metrics reused across parallel workstreams. Discovery for each new application drops to around two weeks.
An AI-enabled delivery lifecycle that stays
- AI evaluations in your CI/CD pipelines flag inefficient patterns and propose refactoring
- Observability measures whether each change actually improves performance and cost
- Requirements, design, code, tests and documentation assisted by agents, with human gates
- Patterns your product teams keep using long after the migration ends
Owned and governed by you
Your platform. Your agents. Your rules.
Availe, our AI business, builds the agentic team inside your environment, so the capability is yours to run, extend and assure, not a black box rented from someone else.
In your tenancy
Built on the AI platform you have endorsed: Azure AI Foundry, AWS Bedrock, Google Vertex AI or GovAI.
Human-led
A dashboard with controls and reporting. Nothing merges or deploys without a person's approval.
Guardrails built in
Role-based access, data access policies, prompt and output review, and versioned models and agents.
Assured
Aligned to whole-of-government AI policy and technical standards, with support through your authorisation process.
- ~70%shorter development cycles, from Availe's multi-agent delivery pipeline in a Commonwealth tenant.
Ways to start
Start with the system you worry about most.
Each way in is scoped and valued on its own.
Legacy portfolio assessment
Every application inventoried and classified by complexity, risk and business criticality, with a prioritised migration backlog.
Transition modelling
Current, interim and target states modelled in Holocentric, so the sequence of change is clear before it starts.
Pilot migration
One application rebuilt by agents in 6 to 10 weeks, proving the pattern in your environment.
AI-enabled delivery uplift
Agents embedded in your software lifecycle and pipelines, with skills transferred to your teams.
Which system are you afraid to switch off?
Start there. We'll model the move, prove the migration on one application, and show you what the rest would take.