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# 5 things I wish I hadn’t done building my AI agent

Assuming a smarter model will automatically fix your AI agent is an expensive trap. Uncover five costly engineering and product mistakes to avoid when scaling AI for production.

- **Speakers:** [Shachar Azriel](https://www.wearedevelopers.com/@shachar-azriel)
- **Event:** World Congress 2026 Europe
- **Published:** July 9, 2026
- **Duration:** 20:16
- **URL:** https://www.wearedevelopers.com/videos/100145-5-things-i-wish-i-hadn-t-done-building-my-ai-agent

## Summary

Building an AI agent at scale involves looking past popular success stories to confront the expensive mistakes that accompany accelerated development. Drawing from the experience of scaling Buzz, an AI-powered code review agent, the narrative unpacks architectural and product decisions that initially seemed reasonable but ultimately cost users and revenue. Rather than relying on theoretical ideals, startup teams must navigate practical pitfalls when deploying real AI coding tools to production, balancing rapid market demands against technical reality.

A foundational misstep in AI product management is relying on industry-standard metrics like time-to-merge, which often obscure true user churn. Meaningful evaluation requires analyzing granular developer behavior to measure precisely whether suggestions were accepted, ignored, or explicitly rejected. Additionally, teams must put ego aside and meet users where they already work. Real adoption begins when the agent integrates naturally into existing decision-making environments, such as GitHub, GitLab, or the IDE, rather than forcing developers into a separate AI application or custom UI.

On the engineering side, assuming an upgraded, smarter model will automatically improve outcomes is a costly trap. Silent model updates from providers can break guardrails, destroy operational consistency, and spit out localized errors that erode user trust. To optimize performance and API costs, startups must deconstruct large workflows, assigning simpler tasks to smaller, cheaper models while reserving high-end models strictly for complex, cross-repository logic. Ultimately, constructing a resilient AI product is "a marathon, not a sprint," requiring sustainable pacing and an active rejection of unsustainable hustle culture to maintain both technical capability and team health.

**Keywords:** ai code review agent, software development lifecycle, developer workflow integration, ai product metrics, user churn analysis, silent model updates, llm guardrails, pull request scanning, api cost optimization, ai workflow deconstruction, ide plugin development, startup scaling failures, software architecture pivots

## Chapters

1. **Chasing product market fit in AI development** (00:00) — AI development creates a false sense of ease when striving for rapid viral growth and revenue.
1. **Building AI agents for software development life cycles** (01:30) — AI tools can automate maintenance tasks like code review and production monitoring to reduce developer friction.
1. **Defining and measuring success for AI coding agents** (04:16) — Capturing transaction outcomes like acceptance, ignorance, and explicit rejection reveals true product viability.
1. **Meeting developers in their existing workflow environments** (08:46) — Forcing developers into a new interface creates friction that prevents product adoption despite strong features.
1. **Protecting the product from silent language model updates** (11:52) — Relying on upstream language models introduces instability that requires strict operational guardrails and benchmarking.
1. **Deconstructing large agent workflows to control infrastructure costs** (15:09) — Analyzing pull requests at scale requires routing complex tasks to robust models and simpler tasks to cheaper alternatives.
1. **Rejecting extreme hustle culture in AI software engineering** (18:02) — Building a sustainable and resilient company requires prioritizing personal well-being over unsustainable sprint mentalities.

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- [Real-world case study of AI agents causing quiet instability](https://www.wearedevelopers.com/videos/1950-the-scrum-master-as-an-orchestrator-guiding-human-ai-collaboration-in-modern-teams) (from "The Scrum Master as an Orchestrator: Guiding Human–AI Collaboration in Modern Teams")
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