1. Home01
  2. About02
  3. Orbit03
  4. Systems04
  5. Agentic05
  6. Stack06
  7. Notes07
  8. Contact08
Director · AI/ML Engineering Currently: agentic platforms

From forecasts to agents in production.

I build the platforms that let AI make real decisions — from million-series forecasting engines to production agent systems.

ZSH · ~/prashant
$whoami prashant — builds agentic AI platforms · 9y decision intelligence · still in the code
Orbits
Scroll · enter orbit
2017 → 2026

02 / About · Now

Engineer-turned-builder of decision intelligence.

Nine years building enterprise decision-intelligence software. I started on distributed forecasting infrastructure — Kafka, Dask, Kubernetes, time-series ML — and grew into leading an agentic AI platform end to end: assistant runtime, workflow engine, memory, retrieval, LLM gateway, guardrails and evals. I still write code every day. I care about systems that are measurable, safe, and boring to operate.

Role
Director, AI/ML Engineering at Aera Technology — hybrid IC + engineering leader
Orbit
~9 years at Aera, 2017 → now
Focus
Agentic AI platforms, with roots in supply-chain forecasting
Long game
Technical leadership of an early-stage AI product. Still in the code.
“Claude Code, configured well, is a workbench — not a chatbot.”
01 · Tools
“A skill borrows your context; an agent creates a new one.”
02 · Agents
“You can't optimize what you don't measure.”
03 · Cost
“Build first. The meetings will come.”
04 · Work
“Trust structured artifacts, not LLM narration.”
05 · Safety

03 / Orbit · Work eras

Seven eras, one system.

Scroll or click a planet

Decision Intelligence

2017

    Era 01 / 07

    04 / Systems

    Systems I've built.

    Two orbits: the forecasting and ML platform I grew up on, and the agentic platform I lead now.

    Inner orbit · Forecasting & ML

    Outer orbit · Agentic platform

    05 / AI · Agentic engineering

    Workbench, not chatbot.

    Claude Code daily across ~40 projects — worktree-per-PR, persistent memory, skills, subagents, hooks, session-cost analysis.

    88%

    Prompt-cache hit rate on a 5M-token session.

    ~430

    PRs authored in one review cycle.

    ~400

    PRs reviewed in the same cycle, across ~25 repos.

    5m→4s

    A multi-system ops lookup, now a slash command.

    Practice

    • Measured

      88% prompt-cache hit rate on a 5M-token session; turned token data into team habits.

    • Ops lookups

      A five-minute multi-system lookup turned into a four-second slash command; wrote the playbook so the team could repeat it.

    • Wiki with a freshness check

      Answers tied to source files, flagged when code changes after the doc was written.

    • AI-native repository spec

      Adopted in multiple repos; internal talk: AI-First Engineering.

    Personal lab · moons

    “A skill borrows your context; an agent creates a new one.”

    06 / Tech stack

    Everything in orbit.

    Hover the belt to pause

    07 / Writing · Notes

    Notes from the workbench.

      Reading shelfDDIA · Fundamentals of Software Architecture · Think Like a CTO · Learning Ray · Modern Time Series Forecasting · TimesFM / TimeGPT / DeepAR papers · Attention Is All You Need

      08 / Contact

      Build first.The meetingswill come.