Resonance4Dots v2-large

AI promises to transform software delivery.

We set out to explore what changes fundamentally.

Delta Deploy is a Diconium initiative exploring what changes when AI becomes part of how we build. Not just in code, but in roles, decisions and team dynamics.

Why we started Delta Deploy

There’s no shortage of opinions on AI in software development. What does “building” now mean at all? We were curious and wanted answers, so we started running experiments across real workflows, building tools to support our own work. What we found goes far beyond tools: it changes the nature of work.

And our findings should not remain unseen. That's why we share our research, buildings, and learnings here in Delta Deploy.

What you see here is not final. It’s a living hub that grows with every finding and data we surface.

The questions we try to answer

Practice of engineering

  • How should requirements, epics, and user stories be structured so AI-enabled development actually delivers value?

  • How do code review, quality assurance, and technical debt change when most code is AI-assisted?

  • What does a high-functioning engineering team look like when humans and agents are both contributing?

Roles, skills, teams

  • How do the roles of PM, PO, and engineering shift?

  • Which experience levels and role profiles do AI-enabled teams need?

  • How do juniors build judgement when the tasks that used to build it are now done by AI?

Frameworks and ways of working

  • What has to change in Scrum, SAFe, and agile frameworks when AI significantly accelerates delivery?

  • Which planning and governance rhythms still make sense when the unit of work is radically smaller and faster?

  • What does “iteration” mean when an agent can produce a working prototype overnight?

Measurement and value

  • Which metrics honestly evaluate productivity, quality, and time-to-market when AI is doing part of the work?

  • What replaces velocity, story points, and lines-of-code as proxies for engineering health?

  • How do we measure outcome that matter like developer wellbeing, code longevity or customer value without drowning in dashboards?

People and the changing nature of work

  • How do we handle the real fears – job security, deskilling, loss of craft – in a way that's honest rather than dismissive?

  • How do data security and intellectual property concerns shape what AI assistance teams can really use?

  • What is engineering as a craft becoming, and what do we want it to become?

  • How do collaboration, mentorship, and team culture change when part of the team isn't human?

Agents we built for ourselves

We have built agents across the software development lifecycle, embedded directly into our workflows to address steps where work slows down or becomes repetitive.

Resonance4Dots v2

Build & Review Agent

How do we prevent implementation, review, and testing from drifting apart and introducing late-stage errors?

  • Refines and implements tickets
  • Reviews and tests code
  • Builds and deploys
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DevOps Agent

How do we avoid finding out about production problems when users already do?

  • Analyzes code, identifies root causes, and suggests fixes while monitoring continuously
  • Reducing unnecessary back-and-forth between 1st/2nd level support and developer in ticket handling
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CloudOps Infra Agent

How do we eliminate slow, ticket-driven infrastructure work and unblock developers?

  • Provisions and manages cloud resources on demand
  • Executes deployments, cleanup, and environment setup
  • Translates natural language into Terraform, Kubernetes, or cloud actions
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Monitor Agent

How do we detect, fix, and contain system issues before they escalate?

  • Continuously checks system health
  • Identifies anomalies and pinpoints root causes
  • Initiates automated recovery and validates system stability
  • Escalates unresolved incidents with full diagnostic context
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GitHub Auto-Heal Agent

How do we reduce CI/CD downtime caused by failing pipelines?

  • Detects and responds to failed workflow runs automatically
  • Pinpoints failure causes across tests, dependencies, and infrastructure
  • Self-heals pipelines through targeted fixes and intelligent retries

What happened when 
an agent joined our team?

We explored it in a real team setup: took a few user stories and ran them the usual way, then again with our Build & Review Agent in the loop.

Based on two representative stories, we compared a standard workflow with four Developers and one Tester and an AI-supported setup integrated into our GitHub + Jira + Copilot stack, where the agent handled implementation and created pull requests, while humans focused on review.

What was the impact?

92,7%

Productivity gain with our coding agent

10 → 0

Reduced human interaction
from 10 to 0

Story 1: Early filtering of vehicles without available functions to prevent unnecessary downstream processing.

Total Time

Before | 2 Days
After | 70 Min

Story 2: Add custom status details to clearly distinguish 422 error causes.

Total Time

Before | 120 Min
After | 15 Min

Meet our data & AI Experts live AND CHAT ABOUT OUR SOLUTIONS AT OUR AI MEETUPS AROUND THE GLOBE