# AI Agents Inside Canonical

> How are we building agents to buy back attention for thesis, diligence, and founder support.

**Published by:** [Anthony Avedissian](https://paragraph.com/@antavedissian/)
**Published on:** 2026-02-20
**URL:** https://paragraph.com/@antavedissian/ai-vc-agents

## Content

While many of our peers are hiring “Heads of AI” to build internal tooling, at Canonical, both Anand and I are building ourselves. Two reasons: nobody understands our workflows, pain points, and preferences better than us, so the best tooling is custom. And, as investors in AI, we think it’s important to feel the curve from the inside - the gap between reading about AI and building with AI daily is massive.We use AI to buy back attention, not outsource judgment. The goal is to automate repetitive, high-volume parts of venture - both things we previously did and found monotonous and things we never had the bandwidth to do - first-principles thesis work, founder support, deep diligence and reference checks, and high-context relationship building. How we use AI at Canonical today: We run an agent that monitors newly published academic papers across frontier tech and AI. It tracks them in a living spreadsheet, tags them by category, and flags the ones that matter based on our internal thesis. This is a deal flow tool: the best founders often show up as authors before they appear in your inbox. Over time, we want this workflow to automatically pull context on authors and labs so outreach becomes faster and higher signal. We run an agent over our email deal flow. Any deal we get sent gets triaged: the system detects whether it is an investable opportunity, extracts key fields into our CRM, and produces a structured readout against our deal criteria. The objective is not to “decide for us,” but to ensure every inbound gets a consistent first pass, and that nothing slips through cracks when volume spikes. We run an agent over call notes and transcripts. It routes them into the right CRM entities (founders, companies, LPs), updates the record, and pulls out the cleanest takeaways. This has become one of the most leveraged parts of our process because it turns conversations into compounding institutional memory. It also feeds directly into how we generate these weekly entries.Where this is going next is even more interesting: agent identity. Most “agentic” systems today still assume the agent is just an extension of the user, operating with the user’s permissions. That model worked for cloud software because it made integrations simple. But systems like OpenClaw reveal the next phase: agents that operate more like colleagues, running independently and in parallel. With this, firms will need scoped access, sandboxes, auditability, and clear blast-radius controls. The winners will not just adopt agent tooling; they will train a small set of firm-specific agents and govern them like real actors inside the org.

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