APEX Agentic Kits: governed AI agents that ship engineering work

28%

AI-generated code on a production codebase

60%

Fewer bugs reaching production

91%

Agent adoption at scale

More than 40% of agentic AI projects are expected to be canceled by 2027

The cancellations come down to cost, unclear value, and weak control. Getting an agent running is easy, but proving its output is correct, complete, and worth the cost is the hard part.

Four gaps an APEX Kit is built to close:

Agents run, but no one checks the work

Behavior is logged, and the agent stays busy, but correctness is not scored, so a wrong diff merges as easily as a right one.

No proof for the board

Quality remains a claim rather than a measured before-and-after showing when delivery improves, or a justification for the spend.

Platform lock-in

Adopting an agent platform means migrating into its environment. You end up owning access to a tool rather than the capability itself.

Cost with no accounting

Token spend climbs across teams with no per-run or per-agent breakdowns, so there is no way to attribute the bill.

Inside an APEX Agentic Kit

Five parts run together inside a Kit, each configured to your environment.

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Component
Workflow
Agents
Control plane
Evaluation engine
Governance and FinOps

What it does
The proven delivery pipeline you select for QA, DevOps/SRE, modernization, or PO work
The specialized agents that run the pipeline
Starts the work on a Jira ticket, PR, deploy, nightly run, or incident.
Scores every output on the L1 to L6 framework, with quality gates before a human merges
Permissions, the autonomy ladder, token and cost visibility, full audit trail

What you configure
Stage sequence and gates
Tool allow-lists
The triggers
Evaluation rubrics, gate thresholds
Autonomy lanes, permissions

APEX Agentic Kits

Kit

What it runs and ships

QA Kit

Test design, execution, and triage, scored across your pipeline

DevOps/SRE Kit

Deployment, reliability, and incident workflows are evaluated before the merge

Modernization Kit

Legacy refactoring and migration, parity checked on every diff

PO Kit

Requirements and backlog turned into testable, measured output

Kit

QA Kit
DevOps/SRE Kit
Modernization Kit
PO Kit

What it runs and ships

Test design, execution, and triage, scored across your pipeline
Deployment, reliability, and incident workflows are evaluated before the merge
Legacy refactoring and migration, parity checked on every diff
Requirements and backlog turned into testable, measured output

The Kit calibrates in two weeks, then runs in your stack

Each Kit tunes to your code and your risk bar first, then runs and moves into your team’s hands.

01 · Calibrate
(2 weeks, fixed cost)

Tune the Kit to your code, risk bar, and triggers.
Set evaluation rubrics and gate thresholds.
Configure the autonomy lanes and tool allow-lists.

02 · Run
(ongoing)

Runs in the stack, IDE, and cloud you already use
Any model, any cloud, no lock-in
Every output is scored before a human merges.

03 · Own
(hand-off)

Run it as shipped, configure it, then own it outright, as versioned rubrics, thresholds, and config in your own repositories
Champions and playbooks stay after we leave.

The 2-week fixed-cost calibration is the structure that keeps a Kit out of that 40%. You see measured output before you commit to running it.

Why engineering leaders choose APEX Agentic Kits over agent platforms

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Capability
Unit
Runs on
What it governs
Evaluations
Proof
Hand-off

Typical agent platform
A platform or environment you adopt
Scoped to one cloud
The agent’s behavior: audit it, stop it
Generic agent quality
Estimated or claimed
You stay on the platform

APEX Agentic Kit
A Kit that drops into your stack
Any model, any cloud, your existing IDE
The work: every output scored before merge
SDLC-specific: ticket testable, differential parity, code committed
Measured production: 0 to 28% AI code, 60% fewer production bugs
Ownership transfers to your team

Greg Svitak

Chief Software Architect, WorkWave

We started the APEX framework with thirty engineers across four teams. Within three months, we were running it across a hundred. The hard problem in AI is not getting a few engineers productive. It is scaling that into a program the whole organization can absorb. N-iX gave us the blueprint.

Greg Svitak

Chief Software Architect, WorkWave

See the evaluations run on your stack

Measured Agentic Kit outcomes

AI-generated code:

0 to 28%

on a production codebase

Production bugs:

60% fewer

reaching production

Agent adoption:

13% to 91%

at scale

Measured on production work during live engagements: before-and-after on the same teams and codebase, against the baseline captured at calibration.

See it scoring your team's output

Case studies from real engagements Case studies

Transportation leader takes AI adoption to 91% and lifts engineering velocity 27% with structured gen AI rollout

AI development services

Case study

Housing management leader cuts bugs reaching production 60% through AI-driven QA modernization

AI development services

Case study

Field service SaaS lifts PR throughput 8x and cuts delivery cycle time 42% with structured AI adoption

Case study

Show all cases

Yaroslav Kisylychka

Head of GenAI Value Lab

APEX Agentic Kit is not something you rent and then depend on us forever. We tune it to how your team works, then hand it to your engineers to run on their own. After we leave, it is still theirs, and that is the part clients tell me they were missing everywhere else.

Yaroslav Kisylychka

Head of GenAI Value Lab

Every output is evaluated. Nothing leaves your environment.

APEX Agentic Kit scores the work before it merges and runs inside controls that a CTO can sign off on and an auditor can inspect. You get the autonomy ladder, L1-L6 evaluations, a full audit trail, and live token and cost visibility for every run.

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It runs on any model and any cloud, right in your existing IDE, so there is no cloud lock-in. The dashboards that prove all of this are live products.