Scaling 1,300+ engineers without slowing down
Tekion builds one of the industry’s most comprehensive automotive cloud platforms — spanning dealership management, consumer retail journeys, OEM integrations, and partner APIs.
With rapid growth came two opposing pressures:
- Quality had to stay consistently high across a mature, deeply interconnected system.
- Engineering velocity couldn’t drop as teams scaled.
Traditional approaches — automation, linting, documentation, training — weren’t enough. They helped at the edges, but they couldn’t reason across Tekion’s massive, multi-language codebase or adapt to dozens of team-level patterns.
Tekion needed something that could act like an engineering teammate, not a text generator.
“We wanted AI that could understand the system, not just autocomplete code,” — Jawahar, Sr. Director of Engineering, Tekion
From fighting the model to controlling It
In early 2024, Tekion began experimenting with commercial AI coding assistants. The team quickly hit common friction:
- Models hallucinated when modifying large features.
- Fixing one issue often broke previously correct code.
- Engineers spent more time prompting than building.
- Test coverage lagged behind rapid implementation.
The insight was simple but pivotal: AI needed structure, roles, and constraints — the same way humans do.
This led Tekion to design its Persona-Driven AI Framework, a system of specialized agents for requirements, architecture, planning, development, testing, and code review.
When Tekion paired this structured approach with Augment’s deep codebase reasoning and context engine, the system became reliable enough to scale across the company.
Why Tekion chose Augment
Tekion evaluated multiple tools, but Augment stood out for three reasons:
1. Deep, reliable codebase context
Tekion’s platform spans thousands of services, internal libraries, and rapidly evolving domain models. Augment’s context engine helped agents reason over:
- Architecture constraints
- Design guidelines
- Cross-service dependencies
- Internal libraries and frameworks
2. Multi-step, persona-based execution
Augment’s CLI gave Tekion the flexibility to encode personas as programmable workflows — not just prompt templates.
Each agent performed a specific role in the SDLC, with clear boundaries and checklists.
3. Integration into Tekion’s existing tooling
Augment’s ability to run in:
- IDEs
- GitLab pipelines
- Internal dashboards
- Jira workflows (in Tekion’s next phase)
meant AI could become part of the engineering process, not an optional add-on.
“With personas, Augment stopped being an assistant and became engineering infrastructure.” — Jawahar
Building a persona-driven engineering system
Tekion’s framework centers on a simple principle: Break every engineering workflow into clear, reviewable steps — then assign each step to a specialized AI agent with a checklist and scoring system.
The core agents Tekion uses today
- Requirements AI — expands abstract Jira tickets into detailed requirements with acceptance criteria and dependency mapping.
- Architect AI — produces HLD/LLD, identifies upstream/downstream impact, and designs flows based on repository context.
- Planner AI — breaks architectures into executable subtasks, generating Jira tickets automatically.
- Developer AI — implements features aligned to guidelines, libraries, tokens, and patterns.
- Test Designer / Developer AI — generates and executes test plans, achieving >90% coverage in days.
- Code Reviewer AI — a fully integrated GitLab L1 reviewer that catches missing null checks, broken flows, PII issues, and requirement mismatches.
Every step has a checklist, a quality score, and a human-in-the-loop validation point — ensuring reliability without slowing teams down.
Results
Across pilots and broad rollout, Tekion saw measurable improvements in both speed and quality.
1. Company-wide adoption: 1,300+ engineers using agents daily
Within three months of rollout, Augment-powered personas became part of the core SDLC for development, QA, and product.
2. Productivity gains: 50% → 85% → 100% on select Jira tickets
Pilot teams saw:
- 50–85% productivity gain using personas
- 80–100% gain on mature teams where personas were fully integrated
- Compared to 30–40% using raw LLM prompting
3. Automated L1 code review: Thousands of PRs reviewed monthly
In October 2025 alone:
- 7,200+ AI review comments
- 35% accepted by developers (driving iterative improvements)
- >94% average quality score across 13,900+ reviews
- Hours saved per day per engineer in back-and-forth review cycles
4. Faster PR lifecycle
After full pipeline integration, time-to-first-review dropped from days to minutes.
5. Test coverage breakthroughs
Tekion achieved 90%+ test coverage on over 20% of their codebase in days, powered entirely by test personas.
Lessons learned
Tekion’s engineering leadership highlighted several key insights:
Start with structure, not prompts.
A consistent framework delivered far more value than ad-hoc prompting or one-off tools.
Treat AI like a real team member.
Each persona has a scope, limits, guardrails, and accountability.
Checklist-driven governance builds trust.
A 94–96% quality score across personas made adoption significantly easier.
Telemetry is essential.
Tekion tracks persona usage, performance, scoring, and deltas — allowing continuous auto-learning and improvement.
“You can’t improve what you don’t measure. Telemetry made the whole system reliable.” — Jawahar
What’s next
Tekion is already building the next phase of its platform:
- Jira-native agent orchestration — assign a Jira ticket to an agent, and the entire workflow runs automatically.
- Security Agent — threat modeling, PII detection, and secure-by-default guidelines.
- Figma-aware front-end development agents — higher-fidelity UI implementation.
- Full Autonomous AI Scrum Teams — agents picking up bugs, planning, implementing, reviewing, and escalating only 20% of cases to humans.
Tekion’s long-term vision is clear:
AI shouldn’t just accelerate development — it should orchestrate it.
“Our goal isn’t AI assistance. It’s AI-driven engineering, with humans guiding quality and decisions.” — Jawahar