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We build your software faster by combining senior engineers with AI coding agents, without compromising architecture, review, or code quality. This is agentic software development as a delivery model: an AI-augmented process in which senior engineers retain ownership of every decision.
TwinCore has elevated the client's customers to the next level of supply chain management. The team is highly cost-efficient from a project management standpoint, and internal stakeholders are particularly impressed with the service provider's team dynamic.
Agentic development is how we build software, not an AI agent product we sell you. Our engineers use AI coding agents throughout the development process to write, review, test, and refactor your software faster. It is AI-augmented software development, where senior engineers stay in control of every decision.
The agents handle repetitive, well-defined work such as scaffolding, boilerplate, test generation, first-pass code review, and mechanical refactoring. Senior developers direct that work and own the parts that matter: system architecture, business logic, and final code quality. AI-assisted software development speeds up implementation while engineers retain ownership of design decisions.
Unlike a manual-first workflow where developers write every line by hand, our engineers use AI coding agents for clearly scoped implementation, testing, and refactoring. This approach is also different from building "AI agents" as a product for your business. That is a separate offering, covered on our AI Agent Development page.
The core reassurance is simple. AI agents write code, humans own the decisions, and every agent-generated change goes through senior developer review before it ships.
How TwinCore delivered a legacy ASP.NET modernization for a land-surveying ERP — migrating to modern .NET and Angular, with Redis caching and no downtime.
How TwinCore built a multi-tenant temperature monitoring platform for cold-chain and pharma compliance — real-time SignalR alerts, audit log, GDP/HACCP-ready reports.
A manufacturer's legacy .NET app rebuilt into a responsive Angular and .NET WMS with FEFO picking, lot and bin tracking, and handheld scanning.
TwinCore built a custom AI agent platform for a US client: 14 reusable agent types for research, data extraction, analysis, and monitoring.
Our team successfully supported and enhanced a custom software solution for a US-based mental health clinic, focusing on improving operational efficiency, treatment tracking, and reporting capabilities. Designed for healthcare professionals, the platform streamlines the management of Electronic Health Records (EHR) and Electronic Medical Records (EMR) while adhering to strict HIPAA regulations.
The largest gains from AI-assisted coding come from implementation tasks that are already specified, not from architecture or judgment. In a controlled GitHub study, developers using GitHub Copilot completed one defined coding task 55% faster (1h11m versus 2h41m). Repetitive, clearly scoped work is where that speed shows up, while human judgment is still needed for complex problems and bugs. Our workflow applies AI agents where they add measurable value while keeping judgment-intensive work human-led.
A strong agentic process does not start when a developer opens Claude Code. It starts with prepared requirements, written rules, repository context files, coding standards, and quality gates. Steps 1 through 3 establish this foundation before AI-assisted implementation begins.
We keep changesets small and the review gate mandatory for a reason. DORA's 2025 research describes AI as an amplifier. It strengthens effective engineering practices, and it magnifies weak testing, poor feedback loops, and unstable delivery processes just as readily. Our workflow combines small changesets, automated testing, and mandatory human review to keep those risks under control.
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Not every task benefits from an AI coding agent, and treating every task as a good fit for AI can accelerate the wrong work. We split the work explicitly.
The distinction is how clearly the task and expected outcome have already been defined. When the expected result is clearly specified and verifiable, an agent can complete the work faster. When the task is deciding what the target should be, or when a wrong decision could cause a security incident or data corruption, the work remains directly owned by a senior engineer.
| Strong fit for AI agents | Human-heavy work |
|---|---|
| Boilerplate and scaffolding | Architecture |
| Isolated features with clear boundaries | Ambiguous business requirements |
| API connectors | Complex distributed workflows |
| Migrations | Security-critical logic |
| Test generation | Concurrency and performance trade-offs |
| Documentation | Production incident decisions |
| Repetitive refactoring |
"Faster development" means little without measurable evidence. We agree on the metrics with you before the first sprint, establish a baseline using your current delivery performance, and report on those metrics at the same cadence as your sprint reviews. That turns the claim into a delivery model you can manage, and it gives you the evidence to stop or expand the engagement.
Lead time for changes and change failure rate are two of the four DORA delivery metrics, so if your team already tracks them, we baseline against your existing numbers instead of inventing a scorecard.
The four business outcomes of an AI-augmented delivery process.
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Where AI-assisted implementation applies across a build.
Full product builds, from MVP to enterprise platform, with AI-assisted implementation across the stack. For AI-first product builds, see our AI MVP Development page.
Agentic development raises legitimate concerns.
Here is how our process addresses them:
AI does not make architecture decisions Senior engineers own system design, technology choices, and technical trade-offs.
AI-generated code does not ship without human review Every change goes through code review by a senior developer before merge. This matters for security. In Veracode's 2025 benchmark of 80 coding tasks across four programming languages and four vulnerability categories, only 55% of generated outputs passed its security checks. This is why AI-generated code must pass human review, automated security analysis, and project-specific testing before merge.
You do not get a "black box" codebase The code stays readable, documented, and maintainable by your internal team or another qualified engineering partner.
Security and compliance reviews remain mandatory Agentic workflows carry the same security review and testing discipline as traditional development.
You still own everything The code, the architecture, and the IP are yours. The AI tooling supports our delivery process; it does not become a dependency in your final system.
Running AI agents against your codebase raises a second set of questions beyond code quality: where does the code go, what enters a prompt, and who can see it. These controls are agreed with you before the engagement starts and documented in the project's security and delivery requirements.
We agree the tool list with you before the engagement starts, and we do not add tools to your project without your sign-off.
Secrets, keys, and live customer data stay out of agent context. Agents work against test fixtures and anonymized data.
AI tools operate within the repository, branch, and environment permissions approved for the assigned engineers.
We use approved commercial or enterprise configurations designed to prevent client code from being used for model training, subject to the selected vendor's terms and privacy settings.
Before work starts, we agree which parts of the codebase agents may read, which they may modify, and which are off-limits entirely.
If you want a specific AI tool excluded from your project, we exclude it and staff the work accordingly.
Every AI-assisted change lands through the normal commit and pull request history, so you can audit what changed, when, and who reviewed it.
Three ways to bring an agentic team onto your work.
A full team of engineers, QA, and an architect uses agentic workflows to build your product from discovery to launch.
Agentic development works across any domain, since the benefits of speed and consistency apply across industries. Our teams pair this workflow with deep domain expertise in logistics, fintech, and enterprise software.
Start with an agentic delivery assessment. It is a focused assessment of your actual codebase and delivery process, and it produces three things:
If the assessment says agentic development is the wrong fit for your codebase, we will tell you, and the assessment findings will remain yours.
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