AI Orchestration
When one AI isn't enough. How multiple agents work together to do real work.
What Orchestration Means
A single AI model is good at one thing: predicting the next token based on training data. Point it at a problem, it gives you a response. That works for chat.
Orchestration is different. It's when you connect multiple agents, tools, and decision points into a workflow that handles real work.
Think of a jazz ensemble. One musician alone can play a melody. Orchestration is how they coordinate: drummer keeps time, bass follows chord changes, horns weave in and out. Each player reacts to what others do. The whole is smarter than the parts.
The reality: Most AI production systems are orchestration problems, not model problems. The model is just one piece. The magic is in how pieces connect and respond to each other.
Three Layers of Orchestration
1. Sequential Workflows
Step A runs. It finishes. Step B reads the output and runs. Then Step C. Simple. Predictable. Good for clear processes with known order.
2. Parallel Execution
Multiple agents run at the same time, not waiting for each other. Bank checking multiple fraud signals. Summarizing a document while extracting key data. They finish independently, then a coordinator makes sense of results together.
3. Adaptive Loops
An agent looks at current state. Decides what to do next. Takes action. Loops until done. The path isn't predetermined. It depends on what happened. Most valuable but also most fragile.
Real Examples from Production
Orchestration in Miru
When you ask for your morning brief, six things need to coordinate:
- Agent 1 fetches school communications (parallel with others)
- Agent 2 checks commute times and travel alerts (parallel)
- Agent 3 scans spending to flag unusual charges (parallel)
- Agent 4 pulls calendar events for today (parallel)
- Agent 5 gets weather and air quality (parallel)
- Agent 6 retrieves your saved articles and news (parallel)
- Coordinator waits for all six, ranks by priority, formats output
Why orchestration matters: Each agent is 100ms slow on average. Run them sequentially? 600ms total. Run in parallel? 100ms total. Orchestration made the brief 6x faster.
The fragility: If any agent times out or crashes, the whole brief breaks. We had to add fallback logic: if the commute agent dies, show a blank spot instead of failing the entire response.
Orchestration at Scale
When you search for a brand, here's what orchestrates behind the scenes:
- Agent 1 searches news databases for recent coverage
- Agent 2 pulls social media sentiment across platforms (parallel)
- Agent 3 fetches funding/investment data (parallel)
- Agent 4 retrieves job postings and hiring signals (parallel)
- Agent 5 grabs YouTube creative ads (parallel)
- Coordinator deduplicates results (same article from multiple sources)
- Ranker scores quality and recency
- Formatter structures into the brand profile page
The hard part: These agents aren't synchronous. YouTube API takes 3 seconds. News search takes 1 second. If we wait for the slowest, the page feels sluggish. Solution: streaming. Show what we have immediately, update as others finish.
Adaptive Orchestration
When a Cowork skill runs (e.g., "Send me a weekly digest of my unread emails"), the agent decides its path as it goes:
- Agent checks Gmail. Finds 47 unread messages.
- Agent decides: too many to summarize in one pass. Needs to filter first.
- Agent categorizes by sender importance. Filters to 12 priority emails.
- Agent checks word count. 12 emails = ~2000 words. Too long for a brief.
- Agent decides to extract bullet points instead of full summaries.
- Agent formats and sends via Slack.
- Agent logs: "Processed 47, extracted 12 priority, sent as bullet points"
Why this is hard: The agent makes decisions at every step. Wrong decision early compounds. It might filter too aggressively and miss important emails. Or include too much and the output becomes useless. We need safeguards at each step: validation, fallbacks, retry logic.
When Orchestration Breaks
Orchestration feels powerful but fragile. Here's where it fails:
Cascading failures: Agent A fails. Agent B depends on A's output and crashes. Agent C was waiting for both. The whole workflow stops. One weak link breaks the chain. Solution: defensive code. Assume any step can fail. Have fallbacks for each.
Latency compounds: One sequential step takes 5 seconds. Add another. Now 10 seconds. User is already bored. Solution: know which steps can parallelize. Measure carefully. Sometimes parallel isn't worth the complexity overhead.
Hallucinations multiply: One agent hallucinates data. Passes it to the next. That agent builds on the lie. By step 5 you have complete fiction. Solution: validate at boundaries. When data moves between agents, check it.
The hard truth: Orchestration is where you stop thinking about "does the model work" and start thinking like infrastructure. Latency. Reliability. Observability. One agent failing silently breaks the whole thing. This is why good orchestration requires monitoring from day one.
Building Orchestration Right
1. Measure before optimizing
Don't assume parallel is faster. Profile it. Find the actual bottleneck. Sometimes one agent is 10x slower and blocking everything. Fix that first. Parallelizing the fast parts saves milliseconds.
2. Isolate failures
Each agent should fail independently. If email fetch fails, the brief should show "emails unavailable" not crash. Wrap each agent in try/catch. Define what "partial success" means for your workflow.
3. Validate at boundaries
When Agent A passes data to Agent B, check it. Is it the right shape? Reasonable values? Missing required fields? This catches hallucinations early before they cascade.
4. Log decision points
When an adaptive agent decides "use option B instead of option A", log it. Later when something goes wrong, you can trace: "Oh, the agent decided to filter too aggressively in step 3." Invisible decisions make bugs impossible to debug.
5. Test with real latency
Test locally where everything is fast. Then test in production where an API might hang. Orchestration that works in local tests breaks in production because timing assumptions are wrong.
Orchestration vs. Prompt Engineering
Prompt engineering is: "How do I get one model to do better at one task?"
Orchestration is: "How do I coordinate multiple tools and agents to accomplish something the model alone can't?"
They're different problems. You can have perfect prompts in a broken orchestration. Or good orchestration compensating for mediocre prompts. Real systems need both.