Research
Vantaverse Research
We're an AI agent software studio, so we research the problems we build against — agent-native architecture, MCP and tool use, multi-agent orchestration, and AI-assisted web development. These are working notes, not settled conclusions — we update them as our client work teaches us more. See also our blog for longer write-ups and case studies.
The Economics of Agent-Native Software
Where does the ROI from AI agents actually show up in a business — and where do teams overestimate it? We're tracking this against real client outcomes.
Last updated 2026-08-22
Error Recovery Patterns in Agent Tool-Calling
Tools fail, return unexpected data, or time out. What an agent does next is where most of the perceived unreliability of AI agents actually comes from.
Last updated 2026-08-01
Guardrails and Policy Engines for Autonomous Agents
Letting an agent act autonomously means deciding, in advance, what it's allowed to do without asking. We're building out our view of what a minimal but real policy layer looks like.
Last updated 2026-07-17
RAG vs. Long-Context Models: Tradeoffs for Production Agents
As context windows grow, some teams are asking whether retrieval-augmented generation is still necessary. Our answer, for now, is: it depends on what you're optimizing for.
Last updated 2026-07-03
How AI Agents Are Changing Web Development Workflows
Agentic coding tools have moved past autocomplete. We're tracking how that actually changes the day-to-day process of shipping a production website or web app.
Last updated 2026-06-19
Agent Memory Systems in Practice
Giving an agent memory sounds simple until you have to decide what to keep, what to forget, and how to retrieve it fast enough to matter. Here's what we're learning building it for real clients.
Last updated 2026-06-05
Multi-Agent Orchestration: When to Split Work Across Agents
Splitting a task across multiple specialized agents sounds elegant, but it adds real coordination cost. We're building a practical checklist for when it's actually worth it.
Last updated 2026-05-22
MCP as the Emerging Standard for Agent Tool Use
The Model Context Protocol is quickly becoming the default way agents discover and call tools. We're tracking how it changes the economics of building agent integrations.
Last updated 2026-05-08
Evaluating AI Agent Reliability Before Production
Benchmarks for raw model capability don't tell you whether an agent will hold up in production. We're building out our own view of what evaluation actually needs to cover.
Last updated 2026-04-24
Agent-Native Architecture Patterns
We're mapping the recurring architecture patterns that separate agent-native systems — where an agent is the primary actor — from software with AI bolted on as a feature.
Last updated 2026-04-10
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