Agentic AI App Modernisation

1. Why bother modernising?

• Old code is a liability. Banks like Morgan Stanley spent decades wrestling with COBOL until an internal agentic tool freed 280,000 developer hours in a single year, proving how much value is trapped in legacy stacks.

• Cloud economics demand elasticity. Running monoliths on‑prem costs more and scales poorly. Modern serverless or container workloads cut infra waste immediately.

• Regulators expect patched, test‑covered systems. Agentic AI can enforce security baselines and compliance rules in real time, something manual teams regularly miss. 

 

2. What exactly is Agentic AI?

“Agentic AI is a system of autonomous agents that pursue explicit goals with minimal supervision.”

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Think of a swarm of specialised bots: one reads code, another writes unit tests, a third scans for vulnerabilities, a fourth runs CI/CD, all orchestrated by a senior architect. This model is rapidly becoming standard, with major pushes from Microsoft, Anthropic, GitHub and Amazon. 

 

3. Introducing Agentic AI Modernisation‑as‑a‑Service

3.1 Outcome‑driven promise

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• Goal: Guaranteed Deliverable (Week 10)

• Refactor: Clean, documented, idiomatic code in a modern language/framework

• Test: ≥ 90 % unit‑test coverage & automated regression suite

• Deploy: Container image or serverless package live in your cloud account

• Handover: GitHub repo, CI/CD pipelines, architecture docs, training session

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3.2 Ten‑week rail (no extensions, no excuses)

1. Week 0 – Discovery & repo boot‑strap (architecture, coding standards, agent instructions)

2. Weeks 1‑2 – UX & architecture hardening (prototypes, cloud landing zone)

3. Weeks 3‑6 – Agent‑driven sprints (legacy conversion, microservice carve‑out, continuous tests)

4. Weeks 7‑8 – Non‑functional blitz (performance tuning, security gates, cost‑optimisation)

5. Week 9 – Full regression & sign‑off

6. Week 10 – Production cut‑over & training

If you don’t review by Monday, the train moves on. Deadlines stay locked.

 

4. Under the bonnet

4.1 AI agent stack

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• Code‑Miner: Parse & map legacy code dependencies (OpenAI GPT‑4o + semantic graph DB)

• Refactor‑Bot: Generate modern code, create PRs (GitHub Copilot Agents)

• Test‑Forge: Write & run unit/integration tests (Playwright/PyTest driven by LLM prompts)

• Sec‑Sentinel: Scan CVEs, enforce OWASP/SANS rules (Open‑source SAST + agentic triage)

• DevOps‑Pilot: Build images, push to registry, deploy IaC (GitHub Actions)

Capgemini, SoftServe and Indium have shown this multi‑agent pattern cuts modernisation effort by 40‑60 %.

4.2 Human in charge

A seasoned architect (think “fractional CTO”) owns design decisions and acts as the single throat‑to‑choke—exactly the Released Group model.

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5. Cloud deployment targets

• Azure: Bicep/ARM and Azure Container Apps

• AWS: CDK & ECS/Fargate

• GCP: Cloud Run & GKE Autopilot

Agentic IaC modules spin these up, run smoke tests, publish endpoints, and hand usage dashboards to your ops crew.

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6. Built‑in quality & compliance

• Automated test coverage before code merges; failing tests block PRs.

• Static/dynamic security scans gate every release.

• Full audit trail lives in GitHub issues, PR threads and agent logs for governance review.

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7. Commercial model

Package - Price (AUD‑ex‑GST) - Scope

• Core: $95 k fixed - Single app ≤ 40k LOC, one cloud target

• Plus: $135 k fixed - Up to 80k LOC, multi‑cloud IaC, SRE hand‑off

• Flex: T&M $260/hr - Enhancements after go‑live

No time‑and‑materials surprises inside the 10‑week rail—only fixed, measurable outputs.

 

8. Next steps

1. Book a 30‑minute scoping call.

2. We scan your repo (read‑only) and produce a 5‑page feasibility report in 48 hours.

3. Sign the SoW & kick‑off the following Monday.

 

Stop patching antiques. Let autonomous agents, hard deadlines and a blunt Aussie approach give you production‑ready, cloud‑native code in ten weeks.