Architecture & Planning
Codebase analysis, best-practice research, and prioritized improvement plans aligned with your architecture.
Agentic Development
Ship features in hours, not weeks. A fleet of autonomous AI agents works in parallel on your codebase — delivering production-ready code, tests, security hardening, UX/UI improvements, and documentation as commits or Pull Requests to your repository.
No new hires. No onboarding. No context switching. Just continuous output.
Same Team, Multiplied
Section titled “Same Team, Multiplied”Agentic development doesn’t replace your engineers — it promotes them. Each developer becomes the human oversight of an agent fleet: setting direction, reviewing every change, and approving what ships. Nothing merges without their sign-off. Same headcount, multiplied output.
Same Team. Multiplied Output.
Every engineer becomes the human oversight of an agent fleet — directing, reviewing, approving.
Before — agile team5 workstreams · blocked by handoffs
Backendwrites code
1 task
Frontendwrites code
blocked
Platformwrites code
1 task
Datawrites code
blocked
QAwrites code
1 task
one task each — handoffs between steps
shipped this sprint:0/ 5
same five engineers↓
After — fleet oversight25 agent workstreams · human-approved
✓
AI Nativeengineer4 agents
✓
AI Nativeengineer4 agents
✓
AI Nativeengineer4 agents
✓
AI Nativeengineer4 agents
✓
AI Nativeengineer4 agents
agents build — AI Native Engineers review & approve
shipped this sprint:0/ 30
features per sprint — same five engineers6× throughput
Agile team
5
Fleet oversight
30
illustrative sprint model — same headcount, every change reviewed and approved by your engineers
The Upgrade Path
Section titled “The Upgrade Path”Becoming AI native is a skill, not a tool swap. Alongside delivery, we train your engineers to run agent fleets themselves — from first supervised tickets to designing their own workflows and quality gates. Your team levels up on real production work, and the capability stays in-house.
From Specialist to AI Native Engineer
A hands-on enablement track, embedded in the delivery cycles — not a classroom.
engineers level up on real production work — the capability stays in-house
Legacy Approach
Section titled “Legacy Approach”One developer picks up a task. Writes code. Waits for review. Addresses feedback. Waits again. Context switches to another repo. Five days later, one feature reaches UAT.
Traditional DevelopmentSequential
One developer, one task at a time, waiting at every step
Pick up task
~2hRead ticket, ask questions, await answers
Time to UAT
1 dev•1 task•sequential
0days
Pick up task
Read ticket, ask questions, await answers
Write code
One developer, one repository at a time
Commit & push
Stage, write the message, push to remote
Related change in another repo
Switch repos — rebuild context, then commit
Open PR, wait for review
Reviewer busy — comments arrive next day
Address review feedback
Fix comments, push again, wait for re-review
Merge and deploy
Resolve conflicts, run CI, deploy to UAT
Current Step
Pick up task
Read ticket, ask questions, await answers
Time to deploy one feature to UAT
0days
1 developer•1 task•sequential
Agentic Approach
Section titled “Agentic Approach”Multiple specialized agents execute in parallel across your entire codebase — planning, building, testing, and documenting at the same time.
Agentic Development Pipeline
Every feature built end-to-end across all components, every cycle
Requirements
Research
Build
Test
Validate
Requirements
Research
Build
Test
Validate
Cycle 1 — continuous loop
Phase 1 of 5
Requirements
Feature scope confirmed across all affected services and components
Full scope identified
All components mapped
No ambiguity before execution
Loop restarts after Validate — continuous improvement
Your Project3/6 active
All components touched end-to-end, every cycle
API Gateway
scoping
Microservice 1
scoping
Database
scoping
UI Frontend
Monitoring
Microservice 2
Mapping feature scope...
The Process
Section titled “The Process”A multi-phase pipeline runs continuously:
| Phase | Outcome |
|---|---|
| Requirements | Feature scope confirmed, gaps identified — no ambiguity before execution |
| Research | Codebase analyzed, implementation plan produced, coverage gaps found |
| Build | Feature implemented end-to-end across all affected services in parallel |
| Test | Full test suite passes, changes reviewed for consistency, documentation updated |
| Validate | Regressions fixed, quality gates passed, ready to ship |
After Validate, the loop restarts. Every cycle makes your codebase better.
The Impact
Section titled “The Impact”| Traditional Team | Agentic Development | |
|---|---|---|
| Time to deliver | ~5 days per feature to UAT | Hours — parallel execution, no waiting |
| Team utilization | 1 developer, 1 task, sequential | Multiple agents working simultaneously |
| Bottlenecks | Blocked by reviews, handoffs, availability | Self-coordinating — no human bottlenecks |
| Test coverage | Written when there’s time | Every change ships with tests |
| Documentation | Outdated within days | Updated automatically, every cycle |
| UX/UI quality | Deprioritized under pressure | Dedicated attention, every cycle |
| Security posture | Periodic audits, reactive | Continuous hardening, proactive |
| Human role | Writes every line by hand, waits in queues | Directs agent fleets — reviews and approves every change |
| Feedback loop | Days (review → comments → fix → re-review) | Minutes (automated validation → instant fix) |
| Scaling | Hire, onboard, ramp up (months) | Scale up in days |
| Cost | Salary + benefits per developer | Predictable, fraction of headcount cost |
What the Agents Cover
Section titled “What the Agents Cover”Full-Stack Implementation
Backend, frontend, API, data layer, security — all system areas covered and scaled to your project’s needs.
Quality Assurance
Tests written with every change. Flaky tests eliminated. Nothing ships without passing quality gates.
UX/UI
Component design, responsive layouts, accessibility, and visual polish. Looks and feels right — not just functions correctly.
Documentation
Documentation always matches the code. Consistency enforced across the codebase.
Scales With You
Section titled “Scales With You”The fleet is tailored to your project — its size, tech stack, and priorities.
Scalable Agent Fleet
Agent count scales to match your project's size and complexity
5
Agents
0
Conflicts
Commits
24/7
Uptime
Built for Your Workflow
Section titled “Built for Your Workflow”You Stay in Control
Section titled “You Stay in Control”Every change arrives as a commit or Pull Request. Review, comment, request changes — exactly how your team works today.
Quality-First Approach
Section titled “Quality-First Approach”Every change passes automated validation before delivery. The full test suite must pass before anything is merged.
Zero Conflicts
Section titled “Zero Conflicts”Agents never interfere with each other. No merge conflicts. No surprises.
Continuous Operation
Section titled “Continuous Operation”No idle time. Agents work in continuous cycles — every cycle improves your codebase.
What You Receive
Section titled “What You Receive”What You Receive
Every improvement delivered as commits or Pull Requests to your repository
Feature Code
New functionality across your codebase
Test Suite
Automated tests, flaky tests eliminated
Security Hardening
Auth, TLS, and observability hardening
Documentation
Docs kept in sync with every code change
Architecture Review
Continuous analysis and planning
your-repo / main
All checks passing
All output delivered directly to your repository:
- Feature code — New functionality across your codebase
- UX/UI improvements — Design, responsiveness, accessibility, visual polish
- Automated tests — Written with every change, flaky tests eliminated
- Security hardening — Auth, TLS, metrics, observability improvements
- Engineer enablement — your team trained to direct, review, and extend the agent fleets
- Documentation — Always in sync with the latest code
- Architecture reviews — Continuous analysis with prioritized improvement plans
- Commits or Pull Requests — Review and merge on your schedule
Case Study: LoomCache
Section titled “Case Study: LoomCache”A release-candidate distributed cache built from scratch in Java 25 — Raft consensus, binary and REST protocol layers, 18 distributed data structures including CP and CRDT primitives, mTLS security, Spring Boot 4.1.0 integration, and chaos-test coverage.
| Metric | Value |
|---|---|
| Production code | 239,000+ lines across 6 modules |
| Test coverage | 37,000+ tests across 1,213 test files |
| Documentation | 30 pages with interactive diagrams, live site |
Browse the LoomCache documentation to see the real product — architecture, API reference, deployment guides, and interactive diagrams. All built by the same process available to you.
LoomCache is an independent open-source project. It is not affiliated with, endorsed by, or sponsored by Hazelcast, Inc. or by any other company whose products are named in this documentation. “Hazelcast” is a trademark of Hazelcast, Inc.; references to it are nominative and describe only migration and comparison. All other product and company names are trademarks of their respective owners and are used for identification purposes only.