I love data science. But, my story is a bit different. 

I began as an Excel-Guru / Mechanical Engineer. Not a traditional background for a data scientist by any means. 

I used Excel until I reached its limits. 

I found R, and the rest is history:

- I began learning R, which led to applying data science to business 

- By applying data science, my team went from $3M/year to $15,000,000 per year

- I began consulting, which led to trainings all over the world

- I found my passion for teaching, and made a company out of it (Business Science!)

Since finding data science, I haven't worked a day in my life. It's been amazing. 

Let me know if you'd like to learn R, Python, or just want to chat data science.

Matt Dancho

Anthony Alcaraz

Writes on ML/LLM Ops 🎨 | Follow me (4,8 K+) 🔔

I help AI startups go from promising to unstoppable.

I also write about it, my book Agentic Graph RAG (O'Reilly, 2026) is the practical blueprint for building AI systems that combine retrieval, reasoning, and autonomous decision-making at enterprise scale.

https://www.oreilly.com/library/view/agentic-graph-rag/9798341623163/

My career has been a journey across both sides of the table: building AI products as a CTO, transforming legacy systems as a Field CTO for enterprises and government, and now at AWS, helping the most exciting GenAI startups scale across Europe and the US.

I've shipped GenAI platforms, deployed MLOps pipelines serving millions, advised L'Oréal on AI-for-BI transformation, and lectured at Oxford on the intersection of LLMs and graphs. But what gets me out of bed is working with founders who are solving hard problems with bold ideas.

At AWS, I connect high-potential startups with the resources, partnerships, and enterprise pathways they need to grow. Before that, I was in the trenches, building, breaking, and shipping.

If you're a founder, VC, or enterprise leader thinking about GenAI, agentic systems, or knowledge graphs, I'd love to chat.

Chief AI Officer & Decision scientist

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

The strongest multi-agent architecture has one rule:
No agent calls another agent. Ever. 💢 

Most agent systems work like phone calls. The orchestrator dials Agent A, waits, dials Agent B with the result, waits again. 

If B hangs up, everyone downstream sits in silence.

Queue-based architecture works like a mailroom. Agents subscribe to event streams. They listen for messages matching their capability. They process, drop output into the next queue. No agent knows another agent exists. The queue is the only shared interface.

Agent A publishes to topic:tasks.parsed.
Agent B subscribes to topic:tasks.parsed.
Agent B publishes to topic:tasks.enriched.
Agent C subscribes to topic:tasks.enriched.

No function calls between them. No shared state. No waiting.

A meta-orchestrator reads every topic. It holds the full pipeline schema. When Agent B crashes, messages pile up in tasks.parsed with no consumer.

It restarts B, scales a second instance, or reroutes. Other agents never notice.

Four layers make this work:

Layer 0 — STATE: External stores (database, graph). Events carry references, not payloads.
Layer 1 — EVENT BACKBONE: Kafka or Redis Streams. Durable, ordered, replayable event log.
Layer 2 — AGENTS: Stateless consumers. One capability each. Subscribe input, publish output. Errors to dead letter queue.
Layer 3 — META-ORCHESTRATOR: Reads ALL topics. Tracks events via parentId chains. Detects stuck agents, poison pills, throughput drops.

Confluent documents four patterns on this backbone: Orchestrator-Worker, Hierarchical, Blackboard, Market-Based. Kai Waehner pairs Kafka with Flink for stateful stream processing. Anthropic's MCP and Google's A2A route through the event broker, replacing point-to-point APIs.

The agent contract is minimal: subscribe to T_in, consume event with id/type/payload/metadata, transform, publish to T_out with parentId linking back. On failure, publish to T_dlq with error context.

Every agent becomes a 200-400 line module. Independently deployable. Independently testable. Replaceable without touching anything else.

Ali Pourshahid at Solace calls this the "agentic AI mesh." Alan Nichol at Rasa warns the alternative is a distributed monolith — agents calling each other are monoliths with extra network hops.

The queue turns coordination into infrastructure. Agents stop calling. They start listening.

Scaling Agentic Startups to Enterprise @AWS | Author of Agentic Graph RAG (O’Reilly) | Business Angel

Your systems of record are lying to your AI agents. 🎶 

Ask sales for ARR: one number. 
Ask finance: different number. 
Ask accounting: something else entirely.

Now tell an agent to "calculate ARR for the board deck."

Which number does it use? When systems drift by 3%, which one wins?

The fragility isn't your model. 

It's whether the agent pulled the right value from the right system at the right time.

🔴 The Core Problem

Your warehouse stores state. It doesn't store reasoning, the logic that connects state to action.
Semantic layers define "official_arr." They don't capture that for board decks you exclude trial ARR, or that finance wins when sales disagrees.
That knowledge lives in tribal memory. And agents can't access tribal memory.

🟡 What Agents Actually Need

Three capabilities your stack doesn't have:
Full recursion, traverse ownership chains of arbitrary depth. 
Ontological inference, derive facts not explicitly stored. PTIME complexity, guaranteed reasoning termination.

SQL can't do this. Standard graph databases can't either.

🟢 The Transition That Works

Most enterprises assume agentic AI requires data migration.

It doesn't.

One bank tried the migration path: 4 people, 1.5 years, still couldn't explain credit decisions.

Same bank with a reasoning layer over existing data: delivered in weeks. 17-minute decisions became 5 seconds.

Bring reasoning to your data. Not data to reasoning.

🔵 The Data Strategy That Emerges

You have two distinct problems requiring two distinct solutions.

1️⃣  Problem one: Your agents need real-time memory. Conversation state. Session context. The ability to blend graph traversal with vector similarity in a single atomic transaction. Data that changes millisecond to millisecond.

This is an operational data problem. SurrealDB solves it. One engine handling graph, document, vector, relational, and time-series with unified ACID guarantees. Your agent's working memory lives here. LIVE SELECT pushes changes instantly. No eventual consistency bugs between separate stores.

2️⃣  Problem two: Your agents need to reason over enterprise knowledge. Derive conclusions from facts spread across Snowflake, Databricks, Postgres, and Oracle. Provide proof trees auditors can verify. Handle recursive ownership chains of unknown depth. Infer what must exist even when data is missing.

This is a reasoning problem, not a storage problem. Prometheux solves it. Connects to your existing data infrastructure without migration. Executes Warded Datalog+/- with mathematical guarantees no warehouse can provide. When your compliance agent needs to explain why a credit decision was made, the proof tree traces from conclusion back to source facts automatically.

These layers don't compete. 

They compose.

Your warehouse remains the analytical source of truth. 
Your operational systems remain the transactional front door.

Scaling AI Startups to Enterprise @AWS | Author of Agentic Graph RAG (O’Reilly) | Business Angel

Context graphs exploded in January 2026. 🤯 

I synthesized 6 leading perspectives into one framework.

Here's what the smartest people in enterprise AI are actually saying:

1. Arvind Jain (Glean CEO) - "Capture the How, Not the Why"

Systems capture final states. But work happens in Slack, docs, and meetings.

The "why" is unobservable. The "how" leaves a digital trail.

Over many cycles, process traces approximate the why.

2. Jaya Gupta (Foundation Capital) - "Security Is Existential"

Decision traces are the moat, but only if you make them safe.

Four primitives needed:
→ Stable pseudonymization
→ Synthesis-level conflict checks
→ Real-time policy enforcement
→ Sanitization before indexing

"Agents collapse the security boundary that human judgment provides."

3. Anshul Gupta (Actively AI) - "Salesforce Has Lost the Deal"

Salesforce is a system of record, not a source of truth.

"Ask any sales leader how they get up to speed on a deal. Salesforce is the last place they'd look."

The right unit isn't the opportunity. It's the account.

Per-account agents compound. Workflow agents don't.

4. Justin Johnson (Run Data Run) - "The Hard Part Is the Clock"

Both ontology camps argue about nouns.

Meanwhile, agents fail because they can't tell time.

"Your CRM knows Sarah is a VP. It doesn't know she was a Director until June—which explains why Finance flagged the pricing exception."

Temporal validity is the missing layer.

5. Diego Lomanto (WRITER) - "Enterprise Brain Must Be Stateful"

Most enterprise AI is stateless, each query starts fresh.

Enterprise brains remember, connect, and compound.

"Being adjacent doesn't compound. To capture reasoning, you need to be there when the decision commits."

Marketing is the proving ground: high volume, inherent ambiguity, unique executions.

6. Dharmesh Shah (HubSpot) - "The Three-Car Garage Problem"

"Building context graphs when you haven't deployed agents at scale is like installing a three-car garage when you don't own a car."

Most companies still:
→ Struggling with basic data unification
→ Figuring out if AI can handle tier-1 support
→ Haven't deployed agents at scale

Context graphs are inevitable. The timing is the question.

Six critical gaps remain:

1. Governance: No confidence decay or contradiction detection
2.*Security: Decision traces harder to anonymize than documents
3. Continuity: Workflow agents don't compound
4. Temporality: Systems can't answer "what was true when?"
5. Statefulness: Most AI is stateless
6. Adoption: Prerequisites aren't met for most companies

The architectural truth:

Layer 7: Context Graphs (memory)
Layer 6: Individual Brain (assistants)
Layer 5: Skills (portability)
Layer 4: Tool Orchestration (typed graphs)
Layer 3: Context Layer (access)
Layer 2: Control Layer (correctness)
Layer 1: Structured Output (composability)
Layer 0: Agent Harness (reliability)

If any layer = 0, total value = 0.

Context graphs are Layer 7. Build from the bottom up.

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