Agentic SDLC
AI-Native SDLC Services
Software Engineering Scaled by Agents, Governed by FDEs and Senior Engineers
Introducing AI into a mature codebase or building a greenfield project shouldn’t feel like rolling the dice on security. Every addition needs rock-solid predictability and strict safety from day one.
STX Next builds software with AI agents inside a controlled process, with written requirements, plans approved by senior engineers, and a security review on every ticket.
STX Next provides AI-native SDLC delivery for companies building software products, platforms and internal tools.
Intent
Plan
Build
Verify
Release
Monitor
Our FDE-led AI Pods run a software development lifecycle where specialized AI agents run the implementation work while engineers own strategy, requirements, plan approval, code review and releases.
Our approach goes further than AI-assisted software development, where individual developers use tools for snippets and search. We deliver at different tiers: AI-Augmented SD or Agentic Engineering, chosen per project based on scope and optimization potential.
AI-native SDLC Tiers
From AI-assisted Software Development to Agentic Delivery
The tiers describe how your project gets delivered: who writes the code, who checks it, and how many people that takes.
We deliver at Tier 2 and Tier 3. Tier 0 and Tier 1 are shown as the baseline, and it’s the same baseline your quote is compared against.
T0 Tier
Classic
Fully manual planning, coding, and testing without AI execution
Team size
●●●●●●○○
6 to 8 people
Time-to-market reduction
Baseline
Cost reduction
Baseline
T1 Tier
AI-Assisted
Developers use individual AI tools for snippets and search
Team size
●●●●●●○○
6 to 8 people
Time-to-market reduction
10 to 15%
Cost reduction
12%
T2 Tier
AI-Augmented
Structured agent workflows process tickets, an engineer reviews and approves every stage
Team size
●●●○○
3 to 5 people (AI Pod)
Time-to-market reduction
20 to 50%
Cost reduction
30%
T3 Tier
Agentic
Critical areas are reviewed and prototypes approved up front, then agents build against the approved plan, including overnight
Team size
●○
1 to 2 people (AI Pod)
Time-to-market reduction
80%+
Cost reduction
60%
Modeled estimates from STX Next’s delivery simulator, compared with a traditional team of 6 to 8 engineers. Your fixed-price quote is set per project after scoping.
How we choose the right model for your project
The tier isn’t something you have to choose upfront.
During scoping, the Forward Deployed Engineer analyzes your requirements, how complex your integrations are and where the risks sit.
We recommend either
Tier 2
AI-Augmented Software Development
When the project needs an engineer checking each stage.
or
Tier 3
Agentic Engineering
When the plan can be approved before building starts.
The AI Pod
Who delivers your project
We size every pod to the project. Larger or more complex builds get a Forward Deployed Engineer as pod lead. On smaller ones, an AI Product Engineer leads and stays your point of contact.
Forward Deployed Engineer
Project owner and your main point of contact. The FDE combines deep technical experience with a solid grasp of your business, and turns your goals into requirements the pod can build against.
AI Product Engineers
Instead of writing every line, an AI Product Engineer runs several agent workstreams at once, works at the level of features and epics, and is accountable for what reaches production.
AI agents
Do the implementation work within the limits the engineers set. The roster covers UX/Design, Premortem/Risk, Architect, Prototype, Coder, Security, DevOps and Eval.
The tools the pod works with
Our AI Product Engineers run agents mainly in Claude Code and Cursor, and use GitHub Copilot and Codex where a project or client setup calls for them. Tools are chosen per project, along with the model for each task.
Claude Code
GitHub Copilot
Cursor
Codex
In 2026 STX Next has achieved the Select partner tier in the Claude Partner Network Services Track.
Nexus: The AI SDLC framework built for a fast start
Every AI Pod arrives with Nexus already in its toolkit. It’s STX Next’s AI SDLC framework, a set of agents, skills and workflows sharpened on earlier projects, so the setup that usually swallows a project’s first weeks is mostly done.
Week one goes into fitting it to your stack, week two into delivery.
Core agents
Each stage of delivery has its own agent, including Spec, Architect, CodeGen and QA. Every one of them handles a single stage of delivery and follows STX Next engineering practices.
Spec
Architect
CodeGen
QA
...
Configured for your project
We pick the agents your scope calls for, connect them to your repositories and environments, and add the rules specific to your domain.
Controls built into the workflow
An engineer approves every plan, agents work on isolated branches with task-scoped context, each change gets a security check, and a person merges and releases. The workflow enforces these steps, so nobody has to remember them.
Reporting every sprint
Each sprint, Nexus reports on pace of delivery, what shipped, quality signals and AI usage. You can see where the time went without chasing a status update.
Pace of delivery
Quality signals
AI usage
Nexus comes with every STX Next AI Pod and isn’t available as a standalone product.
Agentic engineering in client work
The results below come from STX Next engineers working agentically inside clients' existing codebases as extended teams. They show the engineering practice our AI Pods are built on.
UK
Shipping 67% More Sprint Tickets with Human-in-the-Loop AI Guardrails
With the same team of four engineers, migration tickets shipped to production rose from 6 to 10 per two-week sprint. The median time to close a tech debt ticket fell from 3.5 days to 1 day. The setup has run in production for about six months.
An engineer approves every plan, cleanup and a security review run on every ticket, database commands are blocked for the agent, and engineers merge and release every change.
Banking & Government
Client
testimonial
I think they [STX Next] are really ahead of the curve when it comes to AI, how to best use it and how to implement it in everyday life, not just for the gimmicky tasks. The expertise was perfect for what we got out of it.
Insurance
UK
Powering Zego's Development with Claude Code CLI and Custom MCP Servers
To eliminate the daily build issues caused by manual processes, Irish financial services platform Capitalflow Group DAC migrated to a microservices architecture unified under Terraform. Today, every deployment they make is completely consistent and auditable.
Agentic engineering
vs. vibe coding
Only one produces software you can maintain.
Agentic engineering is the practice of building software with AI agents inside a controlled process, with written requirements, reviewed plans, tests and a named engineer who owns the result. Vibe coding means prompting a model until something runs.
Vibe coding
Agentic engineering at STX Next
Starting point
A loose prompt, refined by feel
Written intent and a reviewed plan before code is generated
Specification
The prompt is the spec
Requirements, architecture and acceptance criteria in writing
Review
Whatever compiles gets shipped
An engineer reviews the plan and gates every release
Testing
Manual checks, if any
A test suite is written and run, and every failure is triaged
Accountability
No named owner
The engineer owns the pull request and the production outcome
Engineering Governance
and Safeguards
Handing implementation to agents raises a fair question: what stops one from changing something nobody asked it to touch? These are the controls that answer it.
An engineer signs off every plan.
No code is generated until an engineer has approved the technical plan for the ticket.
One ticket, one branch.
Each ticket is built on its own isolated branch, so nothing reaches your main codebase before it has been reviewed.
Agents only see what the task needs.
Context is scoped to the ticket, keeping agents out of parts of the system they weren’t asked to touch.
Local anonymization.
An internal CLI tool replaces identifiers (names, companies, emails, etc) with synthetic fakes locally, no external APIs are used.
Every ticket gets a security review.
Small tasks receive the same scrutiny as large features.
Agents can’t run database commands.
Databases are blocked for agents, so an agent can’t modify or delete your data directly.
People ship, agents don’t.
An engineer merges every change and releases it to production.
When should you consider AI-native SDLC delivery?
AI-native SDLC delivery suits companies that want a product, platform or internal tool built faster and cheaper than a conventional team would manage, without giving up review and accountability.
We offer AI Readiness Audits, run by our Business Analysts and Senior Engineers, to assess how your teams build software today and help you build an adoption roadmap.
1
Assess
We analyze your AI Readiness during a 3-week audit and agree upon a target tier.
2
Adopt
Bring Nexus into your team. We deploy Nexus into your environment via 1-week bootcamp.
3
Launch
Deliver first project with STX Next’s AI Pod.
4
Scale
Scale the AI Pods and the agents within them. Move towards fully agentic delivery.
AI Bootcamp for your engineers
If you’d like your own team working agentically, we can get them there in a week. Certified STX Next experts will deploy the framework into your environment and train your engineers to operate as an elite AI Pod.
Start your next project with STX Next’s AI Pod
STX Next delivers software through an AI-native SDLC, with engineer-led AI Pods, a fixed price and a quote that compares the cost with traditional delivery.
Tell us what you want to build and we will come back with a recommended tier and a price.
FAQs
What does an AI-native SDLC change about how my project is delivered?
AI agents take over the implementation work, and engineers keep every decision about what gets built and what gets released. An engineer writes the requirements with your stakeholders, approves each plan before any code is generated, reviews the output and releases every change. You work with a smaller team led by one Forward Deployed Engineer, and you approve clickable prototypes before the main build starts. The project runs at a fixed price, and your quote shows what the same scope would cost with a traditional delivery team.
How is agentic SDLC delivery different from AI-assisted software development?
In AI-assisted development (our Tier 1), developers use AI tools for individual tasks and still write and test most of the code themselves. In an agentic SDLC, agents do the implementation across the whole lifecycle, and engineers direct and approve their work. That shift is where the larger time and cost reductions at Tier 2 and Tier 3 come from.
What affects the price of an AI-native SDLC project?
The main factors are the size of the scope, the number and complexity of integrations, the domain, how clear the requirements are, and the tier the project runs at. Every project gets a fixed-price quote after scoping, and the quote shows what the same scope would cost with a traditional delivery team, so you can compare the two directly.
What happens if our requirements change during the project?
The fixed price covers the scope agreed during scoping. If you add or change scope later, we price the change and agree it with you before any work on it starts.
How much of our time does an AI-native SDLC project need?
You need one decision-maker who can approve requirements and prototypes early in the project and review releases as they ship.
What happens to our code and data, and which models are used?
The code is always your property. Personal details are anonymized locally before anything reaches a model, and agents never get database access. We work mainly with Claude and Cursor, sometimes Copilot or Codex, with the specific model chosen per task based on how complex it is. Any model processing data outside your own environment needs your sign-off first.
Can the tier change once the project starts?
We keep fitting the tier to how the project actually unfolds, not just the initial scoping - if the plan can be approved further upfront than expected, we move you from Tier 2 to Tier 3, or back if new risks appear. Any change is re-priced and agreed with you first.
Can our own engineers maintain the software after launch?
Yes. At handover, you get the written requirements, architecture decisions, test suite and deployment setup, so your team or another vendor can take over the codebase. If you'd rather we keep going, we can quote ongoing development and support separately.
