Teams are rushing into multi-agent systems. The demos work.
The production systems don't. The bottleneck is almost never the agent.

The infrastructure layer
is what makes agents reliable.

Fan-out and fan-in. Idempotent tasks. Context isolation. Model routing. Trace IDs across agent chains. Data governance. These are data engineering primitives — and they're what separates a working agent system from a stalled pilot.

Read the thesis Browse patterns
The problem LangGraph defaults and bloated shared context cause latency and hallucination — not the model
The insight Data engineering primitives — idempotency, fan-in/out, observability — map directly to agent infrastructure
The implication The teams shipping reliable agents are the ones who understand what sits underneath

Patterns Library

Agentic systems first. Data infrastructure underneath. Written for practitioners, not demos.

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Agentic Systems 22

Orchestration patterns, context isolation, model routing, evals, and governance for multi-agent systems.
Stream Processing 2

The primitives behind Kafka, Flink, and real-time data pipelines.
Storage & Table Formats 2

Iceberg, Delta Lake, Hudi — why table formats exist and when they matter.
Orchestration 2

From cron jobs to DAGs to asset-centric pipelines.
ML Infrastructure 2

Feature stores, training-serving skew, and the infrastructure behind production ML.
Real-Time & OLAP 2

ClickHouse, Pinot, and the architecture of sub-second analytics.
Observability & Quality 2

Data health, lineage, and the five pillars of observable data systems.
Platform Deep Dives 16

AWS, GCP, Azure, Snowflake, Databricks — honest comparisons and cost traps.
Behind this

Angshuman Rudra

Twenty years across data engineering, AI infrastructure, and platform architecture — including founding-era work at Yahoo and building the data backbone at TapClicks, where the platform processed $10B+ in annual ad spend across 200+ connectors. I've been through enough cloud migrations, warehouse evaluations, and agentic pilots to have strong opinions about what breaks and why. Porino is where I write those opinions down — and work directly with the teams making the decisions that matter.

Advisory

Architecture Clarity Session

Most infrastructure decisions get made wrong because the person making them hasn't seen enough of them go wrong. Before you sign the Snowflake contract, migrate to GCP, or hire three engineers to build what a different tool already does — spend 90 minutes with someone who has.

You get a working session reviewing your current setup and the decision in front of you, followed by a written 1-pager within 24 hours: what we covered, the honest tradeoffs, and what I'd do next.

$750 — fixed. No pitch, no retainer conversation.

If the session surfaces implementation work, I can connect you with a vetted specialist from the Porino network. That's a separate conversation — only if you need it.