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AIDLC
AI writes the code, you approve the merge, nobody loses sleep
Three agents do the work. Every merge needs a human signature. No exceptions, by construction.
Resilient, Scalable Infrastructure
Kloia engineers self-healing infrastructure that automatically detects, adapts, and resolves failures in real time using event-driven automation, container orchestration, and cloud-native best practices. We provide ongoing support across AWS, Azure, and GCP, maintaining performance, cost-efficiency, and compliance as your business scales.
Build AI into your delivery
Most teams we talk to have already tried bolting AI onto their workflow. The pilot worked. Monday didn't. AIDLC builds AI into the pipeline, not on top of it. Sandboxed, audited, every output tied to the spec it came from.
Built for
What it is not
Best suited for
Your AI tools work, but your delivery doesn't
Here is why, and what we did about it.
The Problem
No company context
AI tools write code without knowing your codebase, your specs, your stored procedures, your standards. Generic in, generic out.
Zero audit trail
Nobody knows what changed, by whom, against which spec, at what cost. When something breaks, the log is empty.
Knowledge stays siloed
The senior engineer's head is still the source of truth. The pipeline learns nothing. That person leaves, the knowledge walks out.
Pilot never becomes default
The shiny demo works. Then adoption stalls. The new way of working never replaces the old one.
Why AIDLC?
Faster delivery
Tens of dollars per feature, hours of elapsed time. Not weeks.
Built-in compliance
Every action audited. Every dollar attributed to an issue.
Institutional memory
The pipeline learns. The org's knowledge stops walking out the door.
Faster onboarding
New engineers ramp on a pipeline that already knows the codebase.
A pipeline that improves
Each cycle's retro feeds the next cycle's configuration.
Humans stay in control
Agents propose. Humans approve. Every merge is gated — by construction, not by policy.
Built into your pipeline
Enterprise Context Foundation
We read your codebase, your specs, your stored procedures, your runbooks. Every fact gets a hash. When something changes, we know exactly which agent context drifted. Read-only at this stage. No central cache.
AI Agents Embedded in the Pipeline
Three agents split the work. A PM agent generates and validates the spec. A Coder agent builds test-first on a feature branch. A Reviewer agent checks the PR against acceptance criteria. They act as one bot identity on GitHub. None of them can merge.
Compliance Layer
Every agent run executes in a non-root container behind an egress allowlist. The agent cannot reach what it was not granted. Every action is logged, every token is short-lived, every dollar of model spend is keyed to its originating issue.
Meta-Loop
Each cycle emits cost, run, and outcome telemetry. We review what the parity gate caught, what cost more than it should have, what got parked for human input. The next cycle is configured from what the last one taught us.
Where AIDLC pays off first
Legacy Platform Modernization
Move an old estate to a new platform without losing parity. Agents read the legacy contracts, write the new code, the parity gate proves equivalence.
QA and Test Design
Generate and review test scenarios from specs. The Reviewer agent catches what the human reviewer would miss on cycle 40 of a long sprint.
DevOps and Release Readiness
Validate deployment checks before cutover. The pipeline does not let you ship until the gates pass and the run cost is accounted for.
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Our Tech Stack
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Cloud Providers: Amazon Web Services, Google Cloud Platform, Microsoft Azure, DigitalOcean, VMware.
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Containers: Docker, EXC, Kubernetes
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Infrastructure as Code (IaC): Terraform, Terragrunt, Pulumi.
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Observability: Grafana, Prometheus, Instana, DataDog.
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Developer Platform: IDP with Backstage
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Continuous Integration/Continuous Deployment (CI/CD): GitHub Actions, GitLab CI, Jenkins, Argo CD, Azure DevOps.
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Security: Cloudflare, Snyk, HashiCorp Vault.
What one full cycle produced
These are not universal claims. They are what that one cycle produced, drawn from agent execution logs, GitHub issues, and pull-request history.
8
features shipped
106
agents run
$702
total agent spend
16h
agent time
100%
human-gated merges
What the pipeline shipped
Eight features moved from spec to merged, reviewed code with no human writing application code.
People specified, clarified, and approved. The label-driven state machine never deadlocked or double-launched a run.
What the pipeline surfaced
A consolidated multi-agent review of the merged work raised 14 critical and 81 high-severity findings before any production cutover. The defects concentrated in two areas: legacy adapter contract fidelity,
and the parity harness itself.
This is the quality gate doing its job, not a failure of the method. The same defects in a hand-coded programme would have surfaced far later and far more expensively. We caught them on a throwaway branch for a few hundred dollars and a few hours.
Cloud-Native Expertise
Kloia is an AWS Premier Partner empowering enterprises to achieve cloud-native excellence. With extensive expertise in Kubernetes, serverless architectures, and AWS optimization, we transform legacy systems into scalable, cost-efficient platforms.
- 100+ Cloud-Native Projects
- 350+ AWS Projects
FAQ
How is AIDLC different from existing AI coding tools?
Existing tools paste AI on top of the developer. AIDLC builds AI into the pipeline. The agents act on GitHub events, run in your sandbox, and answer to your spec. They never merge.
Can the agents merge code on their own?
No. Agents propose, a human approves. The strongest action an agent takes is opening a pull request and labelling it for review. Every merge is human-gated by construction, not by policy.
How do you prevent an agent from doing something dangerous?
The agent runs in a non-root container with a one-hour scoped token. All outbound network traffic goes through an egress allowlist enforced outside the agent process. In production, a DNS firewall and AWS Network Firewall sit in front of that.
What does one delivery cycle actually cost?
In a recent engagement, a single feature cost between $30 and $60 of model spend, keyed to its originating GitHub issue. A full cycle of eight features came in under $750. Context caching keeps the unit cost stable as scope grows.
How long does it take to get started?
One to two weeks to build the knowledge base. Then the first cycle runs. We do not need a six-month integration.
Will AIDLC work for regulated sectors?
It's designed for them. Every action is logged, every model run is auditable, every dollar is attributed. We bring the compliance layer with us.
Do you support custom LLMs?
Yes. The platform is model-agnostic by manifest. We default to Claude on Bedrock in the EU, but routes can point to any compatible endpoint, including internal models.
Is AIDLC viable for smaller teams?
We have a focused starter package for teams under 50 engineers. Less compliance scaffolding, same pipeline, faster start.