Blog
Engineering notes.
Long-form writing from the team. No SEO fluff.
AI Agents Look Simple. That's Exactly What Makes Them Hard to Build.
When someone outside the team asks 'Can we just use Claude?', they're usually asking for a whole system — scope, tools, memory, guardrails, retries, cost limits, logging, and evals. Here's what that request actually contains.
By Priya Ramaswamy
One Agent, Many Tools: How We Wire GTM Work Through MCPs
Going all-in on MCPs does not mean dumping 21 tools into a prompt. It means one agent with clear context, constrained tool access, and a repo that holds ICP, playbooks, CRM notes, and call transcripts.
By Marcus Chen
Open-Weight AI Models in 2026: A Builder's Field Guide
Builders have more model choices than ever — open-weight, true open source, and closed APIs. Choosing well means weighing capability, cost, speed, privacy, openness, and deployment — not chasing the single 'smartest' model.
By David Osei
Guardrails, Memory, and Tool Permissions: The Agent Layers That Actually Save You
Most agent failures are not model failures. They are missing scope, weak memory, or tools with too much power. A practical map of the layers that keep production agents from becoming confident accidents.
By Sara O'Neill
Evals, Retries, and Cost Limits: The Unglamorous Agent Stack
The difference between a demo agent and a production agent is usually not the model. It is whether retries, cost caps, logging, tracing, and evals were treated as product requirements from day one.
By Priya Ramaswamy
The Future of AI Agents in Software Development
Agentic systems are moving from demoware to production infrastructure. Here's how we build agents that survive real customer traffic — and where the industry is actually going.
By Priya Ramaswamy
Why Choose a US-Based Development Partner in 2026
The economics have changed. Here's an honest breakdown of what a US software partner actually costs, what you get for that cost, and when it's the wrong choice.
By Marcus Chen
How to Refactor Vibe-Coded Apps for Production
You shipped something amazing in a weekend with Lovable, Cursor, or v0. It has real users. Now what? A practical field guide to hardening AI-built codebases.
By Sara O'Neill
How to Evaluate AI Agents Once They Reach Production
Vibes-based evaluation stops working the moment your agent has real customers. Here's the eval strategy we've deployed at every AI client for the past two years.
By Priya Ramaswamy
Picking the Right Stack for a New Product in 2026
A senior-engineer's honest opinion on what to reach for when starting a new web product in 2026 — and what to avoid.
By David Osei