Agent systems · NYC
TanayShah.
I'm an AI infrastructure engineer. I build the systems behind production chat apps and agent platforms — backends, sandboxes, iOS clients, fast.
CS Honors + Statistics · UMD · grad. Dec 2025
5 roles · 4 years · NYC-based
Agent infra · sandboxes · durable backends
Current: Analyst @ TQ Ventures
Most recent 14-week window: 1,035 commits, 30 PRs merged, work spanning backend, frontend, iOS, and infra at near-100% blame-ownership. Five production roles across four years. The numbers are reproducible — charts in §03, timeline in §04.
I'm Tanay. I build production AI infrastructure — agent loops, sandboxed runtimes, durable event-sourced chat, the iOS clients that hang off them. Production agent infrastructure that ships — and agents that don't just work, they work well. I think about evals as much as I think about loops.
The way I work: pick a hard problem, draw the diagram, ship the first version in days, instrument it, iterate. I think about agent systems for fun — LangGraph + Claude with on-demand tool loading; gRPC bridges between API and slim agent pods; Python sandboxes built on JSON-RPC, AST validation, and seccomp-locked bubblewrap. The diagrams in §02 are real, the numbers in §03 are measured.
// THE LAST CHAPTER
Fourteen weeks as Founding Engineer at Structured AI (NYC), February to May 2026. 1,035 commits across backend, frontend, iOS, plugin work, and infra. Shipped the production document-analysis agent (the shape in §02), a ZDR-compliant code-execution sandbox built on the PTC-interception pattern, an in-house eval harness wired to a public domain benchmark, and the iOS app from zero. Before that: distributed systems at Intuitive Labs SF; two years of ML research at UMD's iSchool fine-tuning Llama on twenty million Wikipedia AfD comments. I started writing CNNs at sixteen.
Graduate of the University of Maryland — departmental honors in CS, Statistics minor, December 2025. The page below is instrumented; you can read it like a spec.
How I architect
agent systems.
Same shape every time: a thin client, an API gateway, a slim agent pod that runs a graph runtime + LLM with on-demand tool loading, retrieval over a vector store, durable state in Postgres + Redis. The diagram below is the reference pattern — portable across agent products.
// THE SANDBOX TRICK
The code sandbox isn't there for safety alone — it's a request interceptor. Hosted code-execution offerings typically ship customer payloads through a third-party endpoint. This sandbox lets the model believe it's executing upstream while the run actually happens on hardened in-house infra — giving you full Zero Data Retention without giving up tool quality. Payloads wipe immediately; the upstream provider never sees them.
- → Layered shape · portable across agent products
- → Streaming end-to-end · WSS · SSE · JSON-RPC
- → Tools discovered at runtime · MCP-shaped
- → Sandbox = interceptor · ZDR by default
What fourteen weeks of me looks like.
1,035 commits, ~74/week median. The chart below is a single 14-week sample of my output cadence — author-filtered to a single email. Calendar dates and milestone labels are stripped out for confidentiality; the bar shape is real.
What I've
written down.
Decision records, not tutorials. Each one takes a default approach, shows where it breaks in production, and documents what shipped instead — with the numbers that justified it.
Tessen: Building the Harness for AI Agents — From Forensic Capture to Runtime Control
Traditional observability treats an agent call like a web request. But an agent is a program that thinks, and it fails in ways a span can't show you. Tessen is the harness I'm building: two lines to capture everything your agent actually does in production, then catch the runaway loop before the bill does.
Why Vector Similarity Alone Lies in RAG (and the Rerank Step Most Pipelines Skip)
Vector top-k retrieval is the standard RAG starting point and it has a known failure mode: high cosine similarity does not equal high relevance. The fix is well-known too, and most production pipelines I read in code review skip it. The two-stage paradigm (broad recall, narrow precision) plus the right reranker buys you 17-40 percentage points of accuracy for ~120ms of latency. Here's the framework.
How to Evaluate a Founding Engineer in 2026: A Playbook for Seed-Stage Founders
Most founders pattern-match founding-engineer hires off senior-IC interview rubrics and end up with the wrong person. Here is the four-dimensional grid I use, the specific signals that predict success, and the anti-patterns that look strong in interviews but stall the team six weeks in.
Structured Outputs vs Tool Calling for LLM Data Extraction: Pick by Intent, Not by Habit
Both endpoints take a JSON schema. Both return validated structured data. They are not the same primitive and picking the wrong one is the most common shape of 'this LLM extraction is unreliable in production' I see in code review. The rule is: structured outputs for extraction and classification, tool calling for triggering actions, and structural validity is not semantic correctness. Here's the framework I use.
Why Prompt-Injection Filters Don't Save You (and What Actually Limits the Blast Radius)
OWASP LLM01:2025 ranks prompt injection as the #1 LLM risk. 73% of 2025 production deployments had a prompt-injection vulnerability and adaptive attack success rates against state-of-the-art defenses exceed 85%. The honest production answer is not a better filter; it's a defense-in-depth architecture that assumes injection happens and limits what the attacker can actually do once it does. Here's what I ship and what I'd ship.
Surface area —
instrumented.
A skill cloud lies. This grid tells the truth: which years used what, how deeply, and where things accelerated. 2026 is the steepest column on the page — that's by design.
The
quick answers.
The same questions come up every time someone finds this site. Here are the answers — concise enough to scan, complete enough to be useful on their own.
Q01What is Tanay Shah doing now?+
Q02Who is Tanay Shah?+
Q03What does Tanay build?+
Q04Where is Tanay based?+
Q05What's Tanay's strongest technical area?+
Q06What was Tanay's role at Structured AI?+
Q07How fast does Tanay ship?+
Q08What's Tanay's tech stack?+
Q09How can I reach Tanay?+
Q10Does Tanay have open-source work?+
Q11How does Tanay pick MCP servers for an agent?+
Q12How does Tanay think about agent sandbox security?+
Q13How does Tanay handle multi-vendor LLM orchestration?+
Q14What's Tanay's view on Zero Data Retention for production AI agents?+
Want to build
something serious?
Agent infrastructure, sandboxed runtimes, evidence-grounded LLM systems, durable backends. If you're building something in that space, I'd like to hear about it. Pick a command:
- $ open mailOpen mail client↗
- $ ssh linkedinConnect on LinkedIn↗
- $ git cloneView source on GitHub↗
- $ open devpostDevpost portfolio↗
- $ cat resume.pdfDownload resume↗