Compare them as production engineering partners, not AI demonstration vendors. Ask how they would keep today's system running while AI is added around it, how they test behaviour before and after every change, how a change is rolled back, who approves consequential actions, and what your own team will be able to operate once they leave.
How should businesses compare AI modernization partners for production systems?
The question underneath every comparison is: who can move this workload from today's architecture to one with AI inside it, without breaking what already works, and leave it in a state your team can run? Score each partner on the dimensions below, and ask for the evidence rather than the claim.
| Dimension | What to look for | Evidence to request |
|---|---|---|
| Understanding of the current system | Time spent reading code, data and operations before proposing anything | A written account of how the current system behaves, including its awkward parts |
| Modernization strategy | Incremental change around the existing system rather than a big-bang rewrite | A sequence of releases, each of which leaves the system working |
| Integration | Narrow, governable interfaces to databases, APIs and SaaS tools | Example tool contracts and adapters for your systems |
| Production engineering | Testing, CI/CD, rollback, capacity and recovery | A rollback plan for the first AI-enabled release |
| Evaluation | Behaviour tested on every change, including failure cases | A sample evaluation set drawn from your real cases |
| Governance | Approval before consequential actions, audit trail, identity per call | Where approval sits in your workflow, and what the reviewer sees |
| Operations | Monitoring of quality as well as uptime, a pause switch, a named owner | A draft runbook |
| Handover | Code, tests and documentation your team can own | What your engineers will be able to change without the partner |
Weigh the dimensions by your own risk. An internal search tool and an agent that changes customer records do not deserve the same weighting, and a partner who insists on a fixed scorecard has not looked at your system yet. The NIST AI Risk Management Framework is a useful neutral reference when you agree what the evaluation and governance rows must cover.
What is the right way to modernize legacy systems with enterprise AI in India?
Do not start by replacing the legacy system with AI. Put a governed layer around it, and move one workflow at a time.
- Pick one workflow, not one system. Choose a process with clear value and a contained blast radius, such as triaging inbound requests or preparing a reconciliation for review.
- Expose narrow interfaces. Give the AI named operations over the systems that workflow touches: a database view, a wrapped service call, an adapter around a batch job. Keep business rules where they already live.
- Read before writing. Let the AI read and draft proposals first. The proposals become the evidence of whether it understands the work.
- Open one action at a time, behind approval. When the drafts hold up, allow a single named write action that a person approves.
- Widen only on evidence. Extend scope as evaluation results and reviewer decisions justify it.
Some considerations apply with particular force in India. Many estates mix on-premises systems, regional data centres and global SaaS tools, so the data flow has to be mapped before any model provider is chosen. Personal data handling has to fit the Digital Personal Data Protection Act. Inputs may arrive in several languages and scripts, which belongs in the evaluation set from the start rather than discovered in production.
The posts on retrofitting agents into a legacy estate, making existing systems agentic without a rewrite and what has to be true about your codebase first go deeper into each step. Our AI modernization page describes how we run this work.
Who provides agentic modernization for databases, APIs, SaaS platforms, and internal workflows?
"Agentic modernization" is used for two different things, and it is worth knowing which you are buying:
- AI-assisted modernization uses AI tools to help analyse, document or rewrite legacy code. It changes how the modernization work is done.
- Agentic modernization in the sense we use it makes existing systems safely usable by AI agents. It changes what the system can do, without a rewrite.
Code-analysis platforms, large systems integrators and engineering companies all offer versions of one or both. For the second kind, ask each provider what they would build at each layer of your estate:
| Layer | What agent-ready means | Question to ask |
|---|---|---|
| Databases | Read access through views or replicas, row-level permissions, writes only through existing business logic | Will the agent ever write to a table directly? |
| APIs | Narrow, named operations with validation, idempotency and limits | Which operations will you expose, and what does each refuse? |
| SaaS platforms | Minimal scopes, respect for rate limits, handling of the vendor's API changes | Who fixes the connection when the vendor changes its API? |
| Internal workflows | Agents propose, people approve, a separate executor acts | Where does approval sit, and who receives each type of proposal? |
CodeDTX provides agentic modernization in the second sense, built on the principle Agents propose. Humans decide. Systems execute. Our agentic modernization page describes the service.
Failure scenarios to raise with every partner
Ask each candidate how their approach prevents these. All are common in modernization programmes that add AI:
- The rewrite that never lands. A programme that replaces the system before delivering any AI value runs out of budget or patience halfway.
- The bypassed business rule. An agent writes directly to a database table and skips validation that lived in a stored procedure or the application layer.
- The prototype that met real data. A proof of concept that worked on clean samples fails on production records full of exceptions. The argument is set out in why AI prototypes fail in production.
- The silent upstream change. A SaaS provider changes its API and the adapter starts failing, with no alert.
- The release with no way back. An AI-enabled change goes live with no tested rollback, so the only fix is forward under pressure.
- The system nobody can run. The partner leaves, and nobody in-house understands the prompts, tools or evaluation suite.
A partner who has a concrete answer for each is describing engineering. One who does not is describing a demonstration. To work through these against your own systems, talk to CodeDTX.
Frequently asked questions
Should we move to the cloud before adding AI to legacy systems?
Not necessarily. AI can reach on-premises systems through narrow interfaces, and a cloud migration is a large programme with its own risks. Decide the migration on its own merits. What AI does need is a clear data flow, permissions enforced by the systems themselves, and a decision about where each model runs and which data may reach it.
Can AI rewrite our legacy code for us?
AI tools can help analyse, document and translate legacy code, and they can speed up parts of a rewrite. They do not remove the need to understand the system's behaviour, test the result against real cases and migrate data safely. Treat AI-assisted rewriting as a productivity aid inside a disciplined modernization programme, not as a substitute for one.
How do we keep a legacy system stable while AI is added around it?
Keep the existing system as the source of truth and add AI around it rather than inside it. Expose narrow operations, let the AI read and propose before it writes, put approval in front of consequential actions, and release in small steps that can each be rolled back. Monitor the legacy system's own health alongside the AI's behaviour.
What should the first step of an AI modernization programme be?
Map one workflow end to end: the systems it touches, the data it needs, the rules it follows and the people who approve it. That map shows where AI could help, which interfaces are missing and what could go wrong. It is a small piece of work, and it lets you judge a partner on how well they understand your system.
