Hunter Brennick AI Systems & Advisory ↗
AI Systems Orchestration
Part I · Foundations/Chapter 00
00

Preface And Map

AI systems orchestration is bigger than agents, bigger than workflows, and more durable than any one framework cycle.

6 min read3 figures
The gist
  • The frontier is not just bigger models. It is bigger models inside better systems.
  • Three worlds to understand: classic orchestration, LLM orchestration, compound AI systems.
  • The endgame is a closed loop: plan, act, observe, verify, recover, improve.
  • Max power means making the system more capable while keeping it inspectable.
THREE OVERLAPPING WORLDS 1 · Classic orchestration the old reliability craft steps · retries · checkpoints 2 · LLM orchestration everything around the model call prompts · tools · memory 3 · Compound AI systems many parts working as one models · tools · checks · humans AI systems orchestration
FIG 00.1Three overlapping worlds: classic orchestration, LLM orchestration, and compound AI systems. The curriculum lives where they meet.

This curriculum aims broader than most: not just prompts, not just multi-agent workflows, and definitely not just "which agent framework is cool this week."

The real vertical is AI systems orchestration.

That means learning how to push AI to the max by arranging models, tools, context, memory, workflows, sandboxes, humans, evals, traces, and feedback loops into systems that can do more than any single prompt or model call can do alone.

The thesis:

The frontier is not just bigger models. The frontier is bigger models inside better systems.

A strong model is the engine. Orchestration is the transmission, steering, dashboard, brakes, pit crew, and telemetry. Without orchestration, even a brilliant model becomes a clever autocomplete box with poor memory and shaky hands.

The Big Picture

There are three overlapping worlds you want to understand:

  1. Classic orchestration: workflows, queues, DAGs, retries, state machines, schedulers, durable execution, controllers.
  2. LLM orchestration: prompts, tools, memory, retrieval, model routing, multi-agent systems, traces, guardrails, evals.
  3. Compound AI systems: multiple models, tools, verifiers, search loops, code execution, feedback, humans, and deployment infrastructure working together.
POWER COMPONENTS · THE REFERENCE STACK Humansdevelopers · operators · domain experts Applicationsweb · internal tools · APIs · automation ORCHESTRATION LAYER planningbreak down goals tool useselect and invoke memorystate and context evaluationscore and assess guardrailspolicy and safety MODEL LAYER reasoning models fast models code models vision open weights … TOOL AND DATA LAYER search databases APIs code execution files other INFRASTRUCTURE compute · storage · networking · monitoring · secrets feedback up control down
FIG 00.3The reference stack: how users, apps, models, tools, and infrastructure compose into one system.

The endgame is a closed-loop system:

THE ENDGAME · A CLOSED LOOP Goalthe work to be done Plannerdecides the next step Context builderselects what the run sees Model routerright model per task Toolsenvironments · actions Artifacts and stateevidence the run happened Evaluatorscritics · checks · scores Human approvalwhere consequences matter Memorylessons for the next run eval feedback steers the model directly
FIG 00.2The endgame: a closed loop that can plan, act, observe, verify, recover, and improve. The same loop as Chapter 0b.

This is what "pushing AI to the max" looks like in practice. It is not one magic agent. It is a system that can plan, act, observe, verify, recover, and improve.

A Better Name For The Field

Use these names depending on the room:

  • AI systems orchestration: broad, serious, and accurate.
  • LLM orchestration: practical engineering layer around model calls.
  • Compound AI systems: the research/architecture framing from Berkeley.
  • Agentic systems engineering: useful when agents are central.
  • AI workflow engineering: good when you mean repeatable operating procedure.
  • Autonomous systems for knowledge work: more product/strategy flavored.

A fitting name for this curriculum:

AI Systems Orchestration: From Workflows To Autonomous Compound Systems

The Core Mental Model

A single prompt is a spark. A serious AI system is a loop.

input
  -> shape the task
  -> gather context
  -> choose model/tool path
  -> act
  -> inspect result
  -> verify against goal
  -> store artifacts
  -> learn from failures
  -> repeat or stop

Every strong AI system answers these questions:

  • What is the goal?
  • Who decides the next step: code, model, human, scheduler, graph, or policy?
  • What context is available, and when is it loaded?
  • What tools can be used, and what are their boundaries?
  • What state is durable?
  • What gets evaluated?
  • What gets traced?
  • What can fail safely?
  • What improves after each run?

The "max power" move is not making the agent more free. It is making the system more capable while keeping it inspectable.

Next chapterChapter 01 · Classic Orchestration The non AI foundation: DAGs, queues, retries, state machines, durable execution, controllers, and the old systems lessons that still matter.