Pearl Technologies, the engineering division powering Charger Logistics, sought to revolutionize its software development lifecycle through AI. By partnering with Augment and implementing Augment Cosmos, Pearl transformed a manual, hardware-dependent workflow into a highly automated AI-SDLC. The results were immediate and profound: a 3x increase in developer productivity, with over 100 PRs opened in the first 30 days and a significant reduction in operational manual work.
The challenge: Bottlenecks in scale and knowledge
With an engineering team of over 100 people spread across the globe, Pearl Technologies faced three primary obstacles to scaling their Transportation Management System (TMS):
- Manual SDLC friction: Developers were limited to a standard 40-hour delivery capacity per week. The process of moving from a ticket to a production-ready PR involved manual story writing, local environment setup, and repetitive unit testing.
- The "knowledge silo" problem: Knowledge transfer was a major bottleneck. Sharing expertise required synchronous calls or extensive documentation, slowing down the pace of collaboration.
- Hardware dependency: Running early agentic AI workflows locally required high-end hardware (GPUs and high memory), creating a physical barrier to entry for many team members.
“Before Cosmos, if I was working on something and needed to share my knowledge, I either had to schedule a call or write everything up and pass it along. We were struggling with session sharing, workspaces, and knowledge continuity.”
The solution: Moving to a cloud-native AI-SDLC
Pearl Technologies piloted Augment Cosmos to move beyond simple chat-based AI and into full-scale agent orchestration. The implementation focused on three core pillars:
1. Agent orchestration via Azure DevOps
The team integrated Cosmos directly with Azure DevOps using webhooks. Instead of manual intervention at every step, the team used a conversational method to issue commands. Cosmos then:
- Automatically wrote user stories based on high-level instructions.
- Generated code and handled unit testing.
- Automatically produced PRs directly to GitHub.
2. Eliminating hardware barriers
By leveraging Cosmos's cloud-native execution (Daemon/VPC orchestration), Pearl Technologies moved the heavy lifting of AI agents off individual laptops and onto Augment's infrastructure. This enabled the entire team to contribute to the agentic workflow regardless of their local machine's specs.
3. Expert workspaces and session sharing
Pearl Technologies utilized workspace segregation to separate front-end, back-end, and DevOps contexts. More importantly, they leveraged Session Sharing, allowing one developer to complete a task and immediately hand over the entire context to another teammate to continue the work.
The transformation: Before vs. after
| Metric | Before Augment Cosmos | After Augment Cosmos |
|---|---|---|
| Productivity per dev | ~40 hours of delivery/week | 70–100 hours of delivery/week (3x boost) |
| Knowledge transfer | Manual calls & documentation | Instant Session Sharing |
| Infrastructure | High-end local hardware required | Cloud-native execution |
| Output (13 working days post-Cosmos) | 0 PRs (team stalled) | 728 PRs completed via Cosmos |
| Ops automation | 100% manual email communication | 60–70% automated via Email Expert |
Deep-dive: Cosmos performance data (analyst review, April–July 2026)
Here is a 4-day pre-Cosmos baseline (Apr 3–6) against a 3-week post-adoption window (Apr 14–30), then assessed 21 agent-driven episodes from June–July 2026.
| Metric | Before Cosmos (Apr 3–6) | After Cosmos (Apr 14–30) | Change |
|---|---|---|---|
| PRs Completed / Day | 0.0 | 56.0 | +∞ (Flow unlocked) |
| PRs Reviewed / Day | 0.0 | 67.5 | +∞ |
| Cycle Time (Complex PRs) | Multi-day (Est.) | Same-day | ~70% Reduction |
| Throughput Efficiency | N/A | 3.2 → 4.7 PRs/session | +47% in 3 weeks |
Key findings
1. Throughput explosion
In the initial baseline period (April 3–6), the team was stalled with 0 PRs completed. After adopting Cosmos, the team delivered 728 completed PRs in just 13 working days. As the team mastered the tooling, efficiency climbed from 3.2 to 4.7 PRs per AI session. This was a 47% gain in three weeks.
2. Drastic cycle time reduction
Complex migrations that previously spanned multiple days now complete within a single day.
3. Agent autonomy & success rate
Across 21 agent-driven episodes (June–July 2026): 71% success, 29% partial, 0% failure. Median duration for a 7-file change was ~5.5 minutes. One agent completed a 16-file carrier driver Kafka integration in 5 minutes, a task that typically takes a senior developer several hours.
4. Qualitative ROI: better code, zero breaks
All 21 agent-driven episodes resulted in zero downstream breakages. AI-assisted PRs consistently included structured root-cause analysis, detailed test plans, and work-item linkage, improving review quality versus manual PRs.
Estimated savings
The analyst estimates the work delivered by agents in just 21 episodes saved between 73 and 113 hours of senior developer labor. Over a full year, this scales to thousands of hours redirected from boilerplate and migrations to high-value architecture and product work.
The impact: Scaling beyond engineering
The success of Cosmos at Pearl Technologies wasn't limited to the core engineering team. Within the first month, the team built a specialized Email Automation Expert for Charger Logistics's operations. By putting AI as the medium for customer communication, they successfully automated 60–70% of manual email conversations, freeing up the operations team for higher-value tasks.
Looking ahead: A new operating model
For Pearl Technologies, Cosmos isn't just a tool, it's a fundamental shift in their operating model. Their goal for the next year is to automate 20–40% of all decision-making within the TMS, moving toward a "Human-in-the-Loop" system where AI agents handle the bulk of operational actions.
“Engineering is moving toward an AI-driven SDLC, whether you're ready or not. Our focus now is on writing great SOPs and expert instructions, and letting Cosmos agents handle the coding. If you want to make a real difference, try Cosmos. The numbers will speak for themselves.”