agent-orchestration-advisor

## Purpose

Guide product managers through designing **multi-agent workflows**—breaking complex, repetitive PM tasks into parallel, specialized AI agents rather than linear, sequential processes or manual execution. Use this to transition from "document-heavy administrator" to "systems-level orchestrator" who coordinates a "living system" of AI agents, human teams, and market data interacting continuously.

**Key Shift:** From linear project management (one task at a time) to orchestration (multiple agents working simultaneously, each with clear boundaries and handoffs).

This is not about prompt writing—it's about **architecting workflows where AI agents handle repetitive research, synthesis, and validation while PMs focus on strategy and decision-making**.

## Input

**Works best with:** The workflow or recurring task you want to orchestrate — described in a sentence or two, however manual or messy it is today.
**Also useful:** Where it breaks down now (too slow, too sequential, too dependent on you), the tools your team already uses, and whether you've worked through [context-engineering-advisor](../context-engineering-advisor/SKILL.md) first (it's the prerequisite discipline).

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended `ARGUMENTS:` line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

**Arriving empty-handed? That works too.** The advisor opens by asking which PM workflow eats the most of your week, then walks the four orchestration dimensions against it.

**Example invocation:** `Design an orchestration for our weekly competitive intel: today one PM spends 6 hours scraping, summarizing, and briefing — sequentially.`

## Key Concepts

### Orchestration vs. Project Management

| Dimension | Project Management | Orchestration |
|-----------|-------------------|---------------|
| **Approach** | Linear oversight of schedules and human tasks | Managing "living system" where AI agents, humans, and data interact continuously |
| **Task Flow** | Sequential (finish A, then B, then C) | Parallel (A, B, C run simultaneously) |
| **PM Role** | Document-heavy administrator | Systems-level leader coordinating automated systems + human judgment |
| **Focus** | Output (features shipped) | Outcome (business results, learning velocity) |
| **Risk Management** | Manual tracking and mitigation | Real-time monitoring with agentic systems flagging gaps |

**Critical Insight:** Orchestration is not about replacing humans—it's about **force-multiplying human judgment** by automating repetitive, time-consuming tasks.

---

### The Four Dimensions of Orchestration

#### 1. **Coordination of Multi-Agent Workflows**
Breaking complex tasks into specialized agents that run in parallel.

**Example:**
- **Manual (Old):** PM spends 8 hours compiling competitive intel, then 4 hours synthesizing customer feedback, then 3 hours identifying roadmap gaps = 15 hours sequentially
- **Orchestrated (New):** Three agents run simultaneously:
  - Agent A: Competitive intel (research agent)
  - Agent B: Customer synthesis (synthesis agent)
  - Agent C: Roadmap gap analysis (analysis agent)
  - Total time: 8 hours (limited by slowest agent), PM reviews outputs in 2 hours = 10 hours total, 5 hours saved

**Key Principle:** Shift from manual selection to **hypothesis orchestration**—agents generate hypotheses, PM validates and decides.

#### 2. **Leadership of Cross-Functional AI Pods**
Governing diverse teams (data scientists, ML engineers, compliance, ethicists) to ensure solutions are scalable, ethical, and aligned.

**What it includes:**
- Embedding diversity-aware workflows
- Risk management (not afterthought)
- Ethical orchestration (ensuring AI doesn't "go rogue")
- Cross-functional alignment (engineering, compliance, design)

**PM Role:** Guardian of Governance—ensures AI systems reflect company values.

#### 3. **Launch Control Tower Function**
Real-time monitoring of organizational readiness across functions using agentic systems to flag gaps before critical failures.

**What it monitors:**
- Support readiness (docs, training, escalation paths)
- Marketing readiness (messaging, assets, GTM plan)
- Operations readiness (infrastructure, scaling, monitoring)

**Key Principle:** Agentic systems act as early warning system—flag gaps before they become blockers.

#### 4. **Strategic Intent Alignment (Context Engineering Applied)**
Feeding AI agents the correct mix of mission, constraints, and priorities to ensure automated decisions reflect company values.

**Connection:** This is **context engineering at the orchestration layer**. See `context-engineering-advisor` for foundations.

**What agents need:**
- Product constraints (what we will/won't build)
- Strategic priorities (what matters most right now)
- Operational definitions (shared glossary)
- Evidence standards (what counts as validation)

---

### The Four AI Management Workflows (Productside Blueprint)

Every PM must master these workflows to move fast while staying grounded:

1. **Context Engineering** ✅ (Foundation)
   - Create AI workspace that remembers product domain, research, JTBD, personas, constraints
   - **Skill:** `context-engineering-advisor`

2. **Synthetic Evals** 📋 (Quality Assurance)
   - Automated validation tests for AI reasoning
   - Generate synthetic data, run workflows against traces
   - Eliminates 80% of hallucination risk

3. **Agentic Workflows** ← **We're here**
   - Agents handle repetitive tasks (competitive intel, customer synthesis, roadmap gaps)
   - PM focuses on strategy

4. **Vibe Coding** 📋 (Rapid Prototyping)
   - Generate clickable prototypes from context workspace
   - Collapse feedback loops from weeks to hours
   - **Connection:** `pol-probe-advisor` (Vibe-Coded PoL Probes)

---

### AI-Shaped Problems (Teresa Torres)

**What makes a problem "AI-shaped"?**
- Previously difficult to scale due to human involvement (e.g.,