Prompt systems
Pattern libraries, system prompts, and modular prompt chains that are easier to maintain, test, and improve.
Hey. I’m Sunil,
AN AI EDUCATOR& APPLIED AIOPERATOR
I help organizations turn generative AI from an interesting experiment into something dependable, useful, and ready for production.
Agentic workflows
Task decomposition, tool-use orchestration, and guardrails so multi-step work completes more reliably.
Eval & governance
Quality gates, golden sets, rubric scoring, and audit trails before systems fail in production.
Team enablement
Playbooks, roles, review loops, and working methods that turn isolated experiments into repeatable capability.
Cited by
Crafting reliable & impactful AI systems
Blended strategy and implementation: prompt systems, agents, evaluation, and the operating model that makes generative AI durable in production.
- 20+
- Years across marketing, cybersecurity, design, analytics, and AI
- $30MM+
- Recovered or saved for businesses over about five years
- 20k+
- PromptEngineering.org subscribers
- Cited
- Across academia, government, and industry
Education & research
PromptEngineering.org
I founded PromptEngineering.org as the education and research home for practical AI literacy: prompt systems, production agentic workflows, and the frameworks teams can actually run. I publish libraries — Partials, Agents, and Miniscripts — and the site reaches 20k+ subscribers.
Visit PromptEngineering.orgEarly foundations
In 2023 I published practitioner architectures on PromptEngineering.org that researchers and builders could build on. This is early scaffolding the field’s later vocabulary — orchestrator–worker, least-privilege context, harness and context engineering, AgentOps — grew around. I did not invent the frontier, and I am not saying later teams copied me.
Jul 2023 · Multi-agent networks
GAINs — coordinator, specialists, validators
In July 2023 I published GAINs: a Central Coordination Agent plus ephemeral specialists and validation/QA agents. An early practitioner multi-agent architecture — a precursor to today’s orchestrator–worker stacks — in the same early window as MetaGPT, and before AutoGen’s mainstream launch narrative.
Aug 2023 · Precursor stack
Precursor to harness & context engineering
In August 2023, before “context engineering” and “harness engineering” became common labels, I published a full practitioner LLM-agent structure: Prompt Recipe + Interface + Tools + Knowledge + Memory (kept separate) + supervisor loop — with the Typical Structure diagram. Research surveys that summer often stopped at Planning / Memory / Tool Use; this stack maps the layers those later disciplines named.
Aug 2023 · Agent architecture
Memory ≠ knowledge
In that same August 2023 agents piece I kept short-term context, long-term memory, and durable knowledge logically separate — so a run can reset memory without wiping knowledge, and the stores stay easier to audit and harder to poison. That split is now table stakes. It pairs with the harness/context precursor; it does not repeat that card.
Read “Keeping Memory & Knowledge Logically Separate”Related: Statistical or Sentient, Aug 14, 2023
Nov 2023 · Privileged flow
HCIN — tiered agents with need-to-know context
In November 2023 I published HCIN as a tiered evolution of GAINs: Primary → Executive → Operational, with privileged, need-to-know context and validation at tier boundaries. Early least-privilege multi-agent design — not a claim that I invented hierarchical agent systems.
Also on the site: System Prompts for LLMsThe 5C FrameworkPartials libraryAgents libraryMiniscripts & Processors
The discipline
AgentEngineering.org
Less hype. More working systems. The site covers the design, tooling, evals, failure modes, and operating practice behind AI agents that have to survive real work. Read foundations first, then mechanics, then AgentOps.
Visit AgentEngineering.orgFoundations
What an agent is, what changes from a plain LLM, and how much autonomy a task actually needs.
Mechanics
How systems decompose work, take action with tools, remember, and reason through multi-step runs.
AgentOps
Traces, evaluations, guardrails, and human controls as an ongoing production practice.
01
What Is Agent Engineering?
The discipline of designing, building, evaluating, and operating goal-directed AI systems that reason over state, use tools, and act under explicit control.
Read on site02
Introduction to AI Agents
Goal-directed software that uses models, tools, context, and control loops across multiple steps — without the hype.
Read on site03
AgentOps: Running Agents in Production
The operating layer that turns traces, evaluations, guardrails, and human controls into a practice for live autonomous systems.
Read on site04
Structured Outputs & Guardrails
Structured outputs constrain shape, guardrails constrain policy, and execution boundaries constrain power. Safe agents need all three.
Read on site
Also on the site: When to Use a Workflow Instead of an AgentTool Use: How Agents Take Action
Next
Let’s make the work operational
For consulting, speaking, or PromptEngineering.org work, start on LinkedIn. Email is available on request.