Your Engineers Use AI Daily. Here’s How to Get More from It

What We Covered
During the Webinar

During the session, we looked at why AI coding tools that solve 70%+ of tasks in demos often drop to 20-30% on real commercial codebases – and what engineering teams can do to close that gap. The focus was on how LLMs and agents actually work under the hood, the core problems they introduce (data bias, “bad token,” “always answer”), and the practical techniques that make AI-generated code reliable in production.

Webinar agenda:

• Market context: where AI in software development stands today and why the agentic shift matters
• What an AI agent actually is – and where agents create real advantage
• How LLMs, agents, and coding tools are trained
• Key problems with ML, LLMs, and agents: data bias, “bad token,” and “always answer” – and how to solve each
• MCP servers and skills: giving AI the right context through grounding, prompting, and structured workflows
• Our toolset that improved AI code generation quality by 20%
• A 5-step approach to rolling out AI coding tools across an engineering organization

Key takeaways:

• AI coding tools fail in predictable ways – and most failures trace back to how LLMs are trained, not bugs in the tools themselves.
• Stop relying on what the model “knows.” LLM intelligence is smoke and mirrors – these tools shine when you feed them context, not when you trust their internal knowledge.
• The shift from IDE-tied to CLI tools means you can run multiple agents in parallel with any IDE of your choice – no vendor lock-in.
• A properly configured open-source stack – indexing, smart search, and second-LLM review – boosts Claude Code output quality by 20% at zero cost.