Blog

Engineering leaders already know how to configure AI agents - the same org design principles that work for teams apply to AI modes, orchestrators, and responsibility delegation.

Learn how engineering teams extract value from unmergeable PRs by treating AI coding agents as research tools that reduce uncertainty and accelerate development.

How product managers, ops, and support teams use AI coding agents to query codebases directly - reducing engineering interruptions and eliminating meeting bottlenecks

How AI coding agents transform bug fixing from a backlog bottleneck into instant beach cleanup - anyone can spot trash, an agent picks it up, engineers review the PR.

Memory banks capture snapshots that drift from reality. Codebase indexing reflects what your code actually is right now, eliminating stale context failures.

Why saturated benchmarks give zero signal when choosing AI coding models, and how to build evals that actually distinguish performance for your team's workflows.

Why code correctness benchmarks miss critical agent failure modes and how to evaluate AI coding agents using work style metrics like proactivity, context management, and communication.

Discover the five-point readiness checklist that predicts whether your AI coding agent will close the loop or leave you as the manual verification layer.

Learn why measuring learning velocity instead of productivity during AI adoption protects your initiative through the change management dip and leads to lasting transformation.

The model exists but the endpoint doesn't. Learn why announced context windows don't match API reality and how cluster economics block long-context inference.

Learn why AI integrations built in 2023 need a complete rewrite, not patches. The scaffolding you built to work around model limitations now prevents you from using current capabilities.

Why centralized AI documentation fails and how engineering orgs can drive adoption through trusted internal influencers and recurring demo forums

Learn why centralizing AI tool spend and measuring output instead of cost unlocks productivity gains - insights from Smartsheet's engineering leadership approach.

Learn why structured, prescriptive rollouts drive faster AI coding tool adoption than open exploration, and how shared defaults get 90% of your team productive.

Understanding why Roo Code's orchestrator spawns subtasks with fresh context windows, and how to manage the visibility gap until tooling improves.

Learn how building a custom mode that asks questions instead of waiting for perfect prompts eliminates prompt engineering and extracts the context your AI coding agent needs through conversation.

Learn how structured to-do lists enable AI coding agents to run autonomously for 45+ minutes without context overflow or tool call failures.

Learn why semantic search outperforms grep for AI coding agents navigating legacy codebases with inconsistent naming conventions, and how it reduces token costs.

Learn why MCP tool descriptions alone are not enough for reliable AI agent behavior, and how custom instructions define when tools should be called.

Learn when to use Orchestrator mode versus Code mode in AI coding agents. Avoid token waste and coordination overhead by matching tool complexity to task scope.

Why forcing every coding task through one AI agent costs more than switching - and how portable rules, short loops, and task-matched tools improve your workflow.

Learn why local AI models excel at scoped code edits but fail at greenfield generation, and how to build a hybrid workflow that balances privacy requirements with agentic coding capability.

Why higher token costs often mean lower task costs - the counterintuitive math of model selection when smaller models spiral through repeated failures.

Why consistent tool call formatting under growing context matters more than benchmark scores for agentic coding workflows