Jiran Zack
Deimel
I build AI systems that think in teams.
I design reasoning and multi-agent systems that break down hard problems, coordinate specialists, challenge their own work, and ship under real-world constraints.
The person behind
the systems.
Most bios start with titles. Mine starts with how I work: I stay with difficult problems, make uncertainty legible, and keep iterating until the system holds. That instinct has carried me through software development at Amazon, government emerging-tech work, OpenAI's Reasoning team, and leading AI transformation inside a financial firm.
It was forged earlier. I fought through school while battling brain cancer and learned that consistency outlasts almost everything. Today I bring that same discipline to founders, executives, and operators who need more than a strategy deck. I stay in the work until the outcome is real. My edge is multi-agent orchestration and aggressive iteration.
Ways I work.
Three engagement models. All of them end with something built, shipped, or running.
Embedded advisory on AI strategy, team upskilling, vendor selection, and implementation oversight. I act as a senior operator inside your company — in the right meetings, making the hard calls, ensuring the work actually gets done.
Typically includes- Executive-level AI strategy & governance
- Vendor selection & procurement oversight
- Team upskilling & hiring support
- Implementation review & course-correction
One 60-minute meeting becomes a working proof in your hands within 24 hours — and a clear path to production within a week. Scope is locked on day one. You leave with something running, not a slide deck or a "next phase" to unlock.
Typically includes- 60-minute scoping session — map the pain, lock the scope
- Working proof in your hands within 24 hours
- Full architecture + production roadmap
- Phased path to production within a week — your repo, your keys
I build or harden the orchestration layer: typed handoffs, observability, budgets, retries, approvals, evals, and the operational controls that make agentic systems trustworthy.
Typically includes- Implementation in your repo and infrastructure
- Tracing, failure recovery, and cost controls
- Human approval and authorization boundaries
- Documentation, training, and clean handoff
One operator.
1,050 agents.
My primary swarm coordinates 1,050 specialist agents across more than a dozen LLMs. I sit in the orchestrator seat—framing the objective, routing work, setting authority boundaries, reviewing evidence, and deciding what ships.
The advantage is not raw agent count. It is disciplined coordination: typed handoffs, model-aware routing, shared but bounded context, adversarial verification, cost controls, and eval harnesses that decide when work is complete. That structure lets me iterate aggressively without confusing motion for progress.
A decade of
high-stakes execution.
Amazon engineering, government emerging technology, OpenAI's Reasoning team, and hands-on AI leadership—experience that taught me to connect frontier capability with operating reality.
On the world stage.
Keynotes, panels, and media appearances at the frontier of AI and emerging technology.
The person behind
the systems.
The same traits show up everywhere: calm under pressure, discipline without theater, service that is practical, and curiosity that refuses to stay in one lane.
Direct questions.
Direct answers.
The short version of how I think about scale, model choice, human control, and the work I take on.
- Specialists are organized by role, capability, and authority.
- An orchestrator decomposes work and routes bounded packets.
- Different LLMs are selected for different reasoning and execution profiles.
- Independent verification and human gates decide what can proceed.
- Some models are stronger planners; others are better critics or tool users.
- Latency, context, cost, and reliability vary by task.
- Model diversity reduces correlated failure and vendor dependence.
- Routing makes capability an architectural choice instead of a brand choice.
- Agents receive bounded tools, budgets, and permissions.
- Consequential actions stop at explicit approval gates.
- Every handoff and decision can be traced and reviewed.
- Humans own objectives, exceptions, and the final right to ship.
- A workflow is too complex for one model or a brittle chain of prompts.
- The team needs better reasoning, routing, evals, or human-approval boundaries.
- A promising prototype must become an inspectable production system.
- Senior judgment and hands-on building need to live in the same person.
Bring me the difficult problem.
Tell me where the system, team, or workflow is getting stuck. I’ll tell you honestly whether I can help.