Sage gives your engineering team governed AI agent workflows across the full SDLC — with full visibility for leadership and zero knowledge loss when engineers move on.
The landscape is moving faster than any team can absorb.
Most engineering teams aren’t failing at AI adoption because they aren’t trying. They’re failing because there is no stable foundation underneath all the change.
01
Tool fatigue is real
02
Knowledge belongs to individuals, not the org
03
Management is flying blind
04
Every tool change is a rebuild
// what changes when you use sage
Three things that matter to the people who approve budgets.
Not a feature list. Three concrete shifts — and two financial advantages that compound automatically over time.
Your prompts survive an engineer leaving
Leadership can see exactly what AI is doing
Swapping tools stops being a project
// two things that compound in your favour over time
Sage Prompts · token efficiency
AI costs go down as prompts get leaner
// leaner prompts · lower model costs · same output quality
Sage Agents · model selection
Agents pick the right model for each task
// right model · right task · no over-spend on inference
// end-to-end or modular
Run the full SDLC. Or just the phase you need.
Sage covers your engineering workflow from story to deployment. Drop it into a single phase where the pain is sharpest right now, or run it end-to-end. No ripping out what already works.
// sdlc coverage · click any phase
end-to-end or standalone
01
Story
Story breakdown
Acceptance criteria
Task decomposition
→
02
Code
Code scaffolding
Convention checks
PR draft generation
→
03
Review
Code quality scan
Security analysis
Standards compliance
→
04
Test
Test case generation
Coverage analysis
Regression checks
→
05
Deploy
Release readiness
Change summary
Rollback assessment
Most teams start with Review or Story — highest immediate ROI — then expand across phases
Sage connects to the tools your engineering team already uses. No ripping out your stack. AI agent capability layered on top of what is already working.
Project Management
Jira
Linear
Asana
Source Control
GitHub
GitLab
Bitbucket
CI / CD
Azure DevOps
Jenkins
GitHub Actions
Code Quality
SonarQube
Snyk
Checkmarx
// how it works
Three components. One plain-English sentence each.
Sage Runner orchestrates. Sage Agents reason. Sage Prompts govern. Each does exactly one thing — which means each can evolve independently as the AI landscape shifts underneath.
Sage Runner
orchestration
Sequences workflow steps, triggers agents in order, logs every input, output, cost, and decision — giving leadership a complete real-time audit trail.
Sage Agents
intelligence
Specialised AI workers — one per SDLC capability. Each selects the right model automatically, calls your tools, and returns structured output.
Sage Prompts
governance
Prompts stored as versioned, immutable assets with an approval workflow before any change goes live. Refined over time — which drives token cost down.
Runner logs on every run: model used · prompt version · token count · cost · latency · output — fully auditable and replayable
// what's built · what's coming
Honest about where we are.
The execution platform is built and in active testing. The surrounding ecosystem — discovery, learning, marketplace — is on the roadmap and being sequenced deliberately.