Third Vector Observer | Edition 3
Agents Need Context, Anthrophic's Growth, SAP Buys Reltio, 1300+ Marketing Skills
Third Vector Observer | Edition 3
Welcome to the Third Vector Observer, an easy-to-digest newsletter featuring 3-5 high-quality, insightful pieces on agentic AI and the impact on organizations and markets. Delivered to your inbox roughly once a month.
Things We Observed...
1. Your Data Agents Need Context (and a Semantic Layer Won’t Cut It)
a16z published a piece that crystallizes something we think is fundamental: data agents keep failing because nobody gave them the business context they need to do anything useful.
The example is deceptively simple. Ask a data agent “what was revenue growth last quarter?” and watch it drown. Which revenue definition? ARR or run rate? Which fiscal quarter? Which table is the source of truth when finance uses fct_revenue but the data team built mv_revenue_monthly? The model can write flawless SQL and still return garbage.
Semantic Layer vs. Context Layer vs. Ontology
These three concepts keep getting conflated, but they’re distinct:
Semantic layer (LookML, dbt metrics): Defines specific metrics in code. Revenue equals X, churn equals Y. Useful but narrow, mapping well to BI dashboards where queries are predictable.
Context layer: Broader. Encompasses metric definitions but adds entity relationships, identity resolution, tribal knowledge, governance rules, and workflow-specific instructions. Think of it as the difference between giving someone a glossary versus six months of institutional knowledge.
Ontology: Formally defines how entities relate to each other. A customer has accounts, accounts have subscriptions, subscriptions generate revenue events. Palantir built a massive business on exactly this idea.
The ontology is the structural backbone; the context layer is the intelligence that makes it operational for agents.
Where the Market Is Heading
a16z maps out three categories of emerging solutions:
Data gravity platforms (Databricks, Snowflake) adding lightweight context features
Existing AI analyst companies pivoting to context-first approaches
Dedicated context layer startups building from scratch
Encoding every business rule into YAML files by hand won’t scale. We believe the winners will crack automated context construction with human refinement.
2. Anthropic’s Revenue Growth Is Breaking the Playbook
Full disclosure: we at Third Vector are massive fans of Claude and have integrated Claude Cowork and Claude Code into how we work. So we’re biased. But the numbers Tomasz Tunguz laid out this week are staggering on their own.
Anthropic added $10 billion in revenue in a single month. That’s twice Databricks’ annual run rate, absorbed in thirty days.
The Trajectory in Perspective
ServiceNow: 20 years to hit $10B annual revenue
Shopify: 18 years
Palo Alto Networks: 19 years
Anthropic: 42 months
Can Anthropic Surpass NVIDIA?
Tunguz’s core question is when, not whether. NVIDIA currently generates $215B in annual revenue at a 22x multiple, producing a $4.8 trillion market cap. At a 25x forward multiple, Anthropic would need roughly $200B in annual revenue to match. The bull case says three years. The base case says four. Even the bear case gets there in seven.
The critical caveat is customer concentration risk. But the sheer velocity is unlike anything the software industry has produced.
What strikes us is what this means for the broader ecosystem. When an AI model company grows this fast, every adjacent category gets pulled forward: infrastructure, tooling, implementation services, data preparation. The companies building agentic workflows on top of these models are riding actual exponential demand.
When Will Anthropic Surpass NVIDIA?
3. SAP Buys Reltio: Do We See A Pattern Emerging?
Salesforce acquired Informatica. Now SAP is buying Reltio. Two of the largest enterprise software companies on the planet looked at their agentic AI ambitions and came to the same conclusion: the data underneath is a mess, and they can’t fix it themselves.
What Reltio Does
Reltio is a cloud-native master data management (MDM) platform that creates a single, consistent view of customers, products, suppliers, and employees across both SAP and non-SAP systems. That last part matters.
SAP’s head of product engineering, Muhammad Alam, was quoted saying:
“AI cannot reach its full potential when data is fragmented across business units, platforms and domains.”
In other words: if your agents can’t trust the data, they can’t do anything useful. SAP launched its Business Data Cloud a year ago, but building clean data infrastructure from scratch is slow. Buying Reltio gives them entity resolution and data harmonization that would have taken years to build internally.
The Bigger Signal
ERP companies are waking up to the fact that decades of bolt-on acquisitions and customization have left enterprise data fundamentally incompatible with autonomous AI workflows. Agentic systems need consistent entities, resolved identities, and governed data. That’s precisely what MDM companies have been building for years, mostly as unsexy infrastructure nobody wanted to pay for. Now it’s suddenly strategic.
SAP Pumps Up Agentic AI and Data Chops with Reltio Acquisition
4. Claude Managed Agents: The Infrastructure Layer Catches Up
Until now, building production-grade AI agents meant stitching together your own sandboxing, authentication, error recovery, and orchestration. Months of plumbing before a single line of agent logic. Anthropic just collapsed that timeline.
Claude Managed Agents, launched April 8, is a suite of composable APIs that handles the operational complexity of deploying cloud-hosted agents at scale:
Secure sandboxing for code execution
Built-in credential management with scoped permissions
Persistent checkpointing that survives disconnections
End-to-end tracing so you can inspect every tool call and decision point
In internal testing, the platform improved task success rates by up to 10 points over standard prompting loops, with the biggest gains on complex, multi-step tasks.
Enterprise Adoption Signals
The early deployments are production, not proofs of concept:
Notion is using it for parallel task execution agents
Rakuten deployed agents across product, sales, marketing, and finance within weeks
Sentry built a debugging agent that diagnoses issues and writes patches
Also notable: the multi-agent coordination capability (in research preview) allows agents to delegate subtasks to other agents in parallel. That’s the architecture pattern everyone’s been talking about, now available as a managed service.
What This Changes
The build-versus-buy calculus just shifted. Security and compliance overhead alone used to be a six-month project. When the model provider handles sandboxing, identity, and tracing natively, the barrier to shipping drops to the quality of your prompts and tool definitions. We’re moving from “can we build this?” to “how fast can we ship it?”
Claude Managed Agents: Get to Production 10x Faster
5. 1,300 Marketing Skills Walk Into an AI Agent
Buron just dropped something that caught our eye: a library of over 1,300 pre-built marketing skills designed to plug directly into Claude and other AI agents. Each skill is a structured knowledge guide covering a specific marketing task, from diagnosing landing page conversion problems to running SEO audits optimized for AI engine discovery.
What’s in the Box
The skills span six categories:
SEO, including audits, schema markup, E-E-A-T authority building
Paid Ads, including Google Ads account analysis and conversion tracking
Content Marketing, including creation, repurposing, optimization
Strategy, including campaign planning and competitive analysis
Analytics, including performance diagnostics and attribution
Social Media, including campaign planning and execution
There’s a community leaderboard where skills get rated, which creates a natural quality filter. The top-rated ones read more like specialist playbooks than simple prompt templates.
Why This Matters Beyond Marketing
We’ve been building custom skills at Third Vector, and the craft involved in writing a genuinely useful skill is non-trivial. You need to encode domain expertise, edge cases, and decision logic. Buron is betting that a marketplace approach can scale this faster than every company building bespoke.
Useful insights! 👍🏻