Figma's engineering team recently shared how they built AI agents to handle security investigations—and the results speak for themselves. Engineers now resolve complex security alerts approximately 70 percent faster, thanks to AI automation that eliminates repetitive manual work.
This isn't a flashy customer-facing chatbot or a speculative research project. It's operational AI delivering measurable productivity gains in one of engineering's most critical—and time-consuming—functions.
For founders building MVPs, Figma's approach offers a blueprint: lean teams can ship faster and do more with less when AI handles the grunt work.
What Figma's Security Agents Actually Do
Figma's agents assist the security team across several high-friction workflows:
- Alert investigation: Agents examine incoming security alerts, pulling context from logs, metrics, and prior incidents
- Historical search: They query past investigations to surface relevant patterns and solutions
- System checks: Agents verify configurations, permissions, and infrastructure state
- Code fix preparation: They draft potential remediation code or configuration changes
The key insight: these agents aren't making final decisions. They're accelerating the investigation and preparation phases, giving human engineers a head start with context, data, and preliminary fixes already in hand.
Why This Matters for Lean Teams
Security work is notoriously interrupt-driven. An alert fires, an engineer context-switches, and the next hour disappears into log spelunking and cross-referencing documentation.
Figma's agents compress that hour into minutes by:
- Eliminating repetitive lookups: No more manually searching Slack, wikis, or ticketing systems for similar past incidents
- Front-loading context: Engineers start investigations with relevant data already assembled
- Suggesting fixes: Agents draft code or config changes based on historical resolutions
The 70 percent time reduction isn't about replacing engineers—it's about giving them leverage. A two-person security team can now handle the workload of three or four.
For founders, this model extends beyond security. Customer support, incident response, compliance reviews, and internal tooling all share the same pattern: high-frequency, low-creativity tasks that drain velocity.
The Compounding Advantage: Agents That Learn
Figma's agents improve over time by learning from prior investigations. Each resolved incident becomes training data. The system gets smarter, faster, and more attuned to the team's specific environment and threat landscape.
This creates a defensible moat. The longer the system runs, the harder it becomes for competitors to replicate. Your agents know your codebase, your infrastructure quirks, and your team's preferred remediation patterns.
If you're building an MVP for engineering or security teams, focus on workflows where historical data creates compounding value:
- Incident response platforms that learn from past tickets
- Code review tools that adapt to team conventions
- Deployment assistants that remember rollback patterns
Show investors concrete efficiency metrics—time-to-resolution, ticket volume handled per engineer, error rate reductions—not vague promises about "AI-powered" features.
Key Takeaways
- AI agents deliver measurable productivity gains in operational and security workflows, not just customer-facing products
- Figma's security agents cut alert resolution time by ~70%, giving lean teams the capacity to ship faster with fewer resources
- Focus on automating high-frequency, low-creativity tasks where engineers spend hours on repetitive lookups and context-switching
- Agents that learn from historical data create a compounding advantage and a defensible moat over time
- Investors and customers respond to concrete metrics: show time-to-resolution improvements, ticket volume handled, or error rate reductions
Build Your MVP in Days, Not Months
If you're building internal tools, security products, or AI-powered workflows, speed to market matters. The faster you ship a working MVP, the faster you validate assumptions, land early customers, and iterate based on real usage.
That's exactly what TechAhir does: we build full, working, sellable MVPs in 3 days—not throwaway prototypes. Senior developers lead every project, ensuring you ship with discipline, not vibe-coding. Virtually zero defects via customized-model QA. Real products, real speed.