Getting Started with Compound AI: A Practical Guide for CTOs

After publishing my article on the compound AI revolution, I received dozens of messages from CTOs and engineering leaders asking the same question: “This sounds transformative, but where do I actually start?”

It’s the right question. Understanding that compound AI systems represent the future is one thing. Building one that delivers real business value is another entirely.

Having guided several organizations through their first compound AI implementations, I’ve seen what works, what fails, and why. This isn’t theoretical—it’s a field guide based on actual battle scars.

The Implementation Trap Most Organizations Fall Into

Here’s the pattern I see repeatedly: A leadership team gets excited about compound AI. They envision an ambitious system that will transform their entire operation. They assemble a team, set a six-month timeline, and aim to build something comprehensive from day one.

Six months later, they have an expensive proof-of-concept that doesn’t quite work in production, a burned-out team, and waning executive enthusiasm.

The fundamental mistake? Trying to build the orchestra before you can play an instrument.

Compound AI systems are powerful precisely because they orchestrate multiple specialized components. But that orchestration complexity is also their Achilles’ heel. Each additional component multiplies the potential failure modes, debugging challenges, and integration headaches.

The counterintuitive truth: The path to a sophisticated compound system starts with something almost embarrassingly simple.

The Crawl-Walk-Run Framework

Successful compound AI adoption follows a deliberate progression:

Crawl: Single-Component Enhancement (Weeks 1-4)

Start by adding one AI capability to an existing workflow. Not a new system—an enhancement to something that already works.

Examples that consistently deliver quick wins:

Intelligent Document Processing: Take your existing document intake process. Add a specialized model that extracts structured data from PDFs or scanned documents. Don’t try to revolutionize your entire document workflow—just make one step dramatically better.

One financial services company I worked with started here. Their loan officers spent hours manually transcribing information from mortgage applications into their system. We added a single document extraction component that fed into their existing process. Implementation time: three weeks. Time savings: 12 hours per loan officer per week. ROI: positive in month one.

Semantic Search Over Internal Knowledge: Your company has wikis, documentation, Slack archives, and shared drives full of institutional knowledge. Employees can’t find what they need. Add vector search with a simple retrieval interface. That’s it.

A healthcare tech company implemented this for their customer success team. Instead of building an elaborate AI agent, they just made it possible to ask questions in natural language and get relevant documentation. Support ticket resolution time dropped 40%. Cost: minimal. Complexity: low.

Automated Classification and Routing: You have incoming requests, support tickets, or documents that need to be routed to the right team. Add a classifier that does this automatically, with human verification initially.

The key insight: these single-component implementations teach your team how to work with AI, establish baseline metrics, and prove value before you attempt orchestration.

Walk: Simple Two-Component Systems (Weeks 5-12)

Once you have one component working reliably, add a second component that naturally complements the first. This is where you begin practicing orchestration, but with minimal complexity.

The classic pattern: Retrieval + Generation.

Take that semantic search system you built. Now add a generation layer. Instead of just returning documents, the system retrieves relevant context and generates a synthesized answer with citations.

This is RAG (Retrieval-Augmented Generation) in its simplest form, and it’s the gateway drug to compound AI. You’re now orchestrating two components: a retriever and a generator. You’re managing context, handling errors, and thinking about how to present results.

Another winning two-component pattern: Generation + Verification.

An insurance company wanted to automate policy document review. Their first instinct was to build a complex multi-agent system. Instead, we started simple: one model to extract key clauses, another model to verify those extractions against a checklist of required elements. Two components, clear handoff, measurable accuracy.

The beauty of two-component systems: they’re simple enough to debug but complex enough to teach you orchestration principles.

Run: Multi-Component Orchestration (Month 4+)

Only after you’ve successfully deployed simpler systems should you attempt sophisticated orchestration. Now you’re ready for systems with 5+ components, complex reasoning loops, and autonomous operation.

This is where you build the coding assistants that execute and debug their own code, the research systems that query multiple databases and synthesize findings, or the customer service agents that can access tools, query internal systems, and hand off to humans when needed.

But here’s the critical point: by the time you get here, your team has already shipped multiple AI systems to production. They understand the failure modes, the debugging approaches, the cost structures, and the user experience considerations. They’re not learning orchestration theory—they’re applying hard-won experience.

The Three Projects That Make Sense First

Not all compound AI projects are created equal. Some are natural starting points; others are minefields for inexperienced teams.

After watching numerous organizations navigate this, three project archetypes consistently succeed as initial implementations:

1. Internal Knowledge Assistant (Lowest Risk, Fast ROI)

Build a system that helps your employees find and understand internal information. This checks every box for a first project:

Low risk: It’s internal, so mistakes don’t face customers. You can iterate based on employee feedback without reputational damage.

Clear value: Everyone knows the pain of searching for information across multiple systems. Relief is immediately felt.

Measurable impact: Time saved per query, reduction in “do you know where I can find…” Slack messages, faster onboarding for new employees.

Forgiving users: Your employees understand it’s a v1. They’ll provide constructive feedback rather than abandoning the tool after one mistake.

Natural expansion path: Start with simple Q&A, add document generation, then add the ability to take actions based on retrieved information.

One manufacturing company started with an AI assistant for their engineering documentation. Engineers could ask technical questions and get answers grounded in spec sheets, CAD files, and past project documentation. Within three months, it became their most-used internal tool. Within six months, they expanded it to handle RFQ responses by pulling relevant past proposals.

2. Document Intelligence Pipeline (Clear Metrics, Immediate Savings)

Take any document-heavy process in your organization—invoice processing, contract review, application intake, compliance documentation—and make it intelligent.

Why this works as a first project:

Quantifiable ROI: You can measure exactly how much time the manual process takes and calculate savings to the penny.

Ground truth available: You have historical documents and known correct outputs, making it easy to evaluate accuracy.

Incremental deployment: Start with AI assistance (human reviews everything) before moving to full automation for high-confidence cases.

Compound AI is natural fit: Document processing inherently needs multiple components—extraction, classification, validation, error handling, human escalation.

A legal tech company built a contract review system that started simple: extract key terms, flag unusual clauses, and surface to attorneys for review. As confidence grew, they added components: compare against standard templates, check for regulatory compliance, generate redline suggestions. Each addition delivered measurable value.

3. Data Analysis Assistant (High Value, Controlled Environment)

Build a system that helps stakeholders query and understand your company’s data without waiting for data analyst availability.

This works because:

Controlled environment: The system operates within your data warehouse with defined schemas and access controls.

Verification is built-in: Generated SQL queries can be reviewed before execution. Results can be sanity-checked against known metrics.

Huge frustration relief: Business stakeholders can get answers in minutes instead of submitting tickets and waiting days.

Natural complexity: This requires orchestration (understanding questions, translating to SQL, executing queries, interpreting results, generating visualizations) but with clear success criteria.

A retail company deployed this for their merchandising team. Buyers could ask “What were our top-selling products in the Pacific Northwest last quarter?” and get back structured results with visualizations. The system generated SQL, executed it, checked results for reasonableness, and presented findings. It didn’t replace their data team—it freed them from repetitive queries to focus on complex analysis.

The Build vs. Buy Decision Framework

One of the most consequential early decisions: should you build your compound AI system from scratch or assemble it from existing components and services?

The industry’s dirty secret: most organizations should buy far more than they think.

Here’s my framework for making this decision:

Build when:

The orchestration logic is your competitive advantage (your specific workflow creates differentiated value)
You have truly unique data or domain requirements that generic tools can’t address
You have ML/AI engineering expertise in-house and capacity to maintain custom systems
The problem is sufficiently important to justify ongoing investment in custom development

Buy (or use managed services) when:

The capability you need is table stakes, not differentiation (e.g., document OCR, semantic search)
Proven solutions exist that handle 80%+ of your use case
You’re building your first compound AI system (use this to learn before building custom)
Maintenance and updating would be a burden for your team

The hybrid approach (which I recommend for most): Use managed services for commodity capabilities (embedding models, vector databases, LLM APIs) while building custom orchestration logic that encodes your specific workflows and business rules.

A healthcare company wanted to build a clinical documentation assistant. They built: custom orchestration logic for their specific clinical workflows, integration with their EHR system, and specialized verification rules for medical accuracy. They bought: the underlying language models (via API), vector database for patient history search, and speech-to-text transcription. Result: 60% faster development than building everything, with customization where it mattered most.

The Real Costs (And Where Organizations Underestimate)

Let’s talk about money, because this is where many organizations get blindsided.

The obvious costs: LLM API calls, vector database hosting, specialized model access, compute for code execution environments.

These are actually manageable. A well-architected compound system can operate remarkably cost-effectively by using smaller models for simple tasks and reserving expensive models for complex reasoning.

The hidden costs that sink projects:

Context engineering: You’ll spend more time than you expect figuring out what context to pass to each component, how to structure prompts, and how to maintain conversation state. This is skilled work that requires both AI expertise and deep domain knowledge.

Integration complexity: Connecting your compound AI system to existing tools, databases, and workflows is messier than you think. Legacy systems have undocumented quirks. APIs change. Authentication becomes a nightmare. Budget 2-3x what you initially estimate for integration work.

Evaluation and testing: How do you know if your compound system is working correctly? You need evaluation frameworks, test datasets, and ongoing monitoring. Many teams realize too late that they can’t ship to production without this infrastructure.

Iteration and refinement: Your v1 will need significant refinement based on real usage. Users will find edge cases you never considered. The system will fail in unexpected ways. Budget for ongoing iteration, not just initial development.

Human-in-the-loop infrastructure: Most compound AI systems need human review, feedback mechanisms, and escalation paths. Building good UX for this is harder than building the AI itself.

A financial services firm budgeted $150K for their first compound AI project. The actual cost: $280K, with the delta almost entirely in integration work and evaluation infrastructure they hadn’t anticipated. The good news: their second project cost exactly what they budgeted, because they’d learned what to plan for.

Common Failure Patterns (And How to Avoid Them)

Having autopsied several failed compound AI projects, the failure modes are remarkably consistent:

The Boil-the-Ocean Syndrome

What happens: Team tries to build a comprehensive system that handles every edge case from day one.

Why it fails: Complexity explodes. Integration points multiply. The project never ships because there’s always one more scenario to handle.

The fix: Ruthlessly scope the MVP. Ship something that works perfectly for 60% of cases and gracefully fails (with human handoff) for the rest. Expand coverage over time based on actual usage patterns, not imagined scenarios.

The Prompt Engineering Rabbit Hole

What happens: Team spends months perfecting prompts, trying to get one model to do everything through prompt engineering alone.

Why it fails: They’re trying to build a compound system with a single component. They hit the ceiling of what prompting can achieve and conclude “AI isn’t ready yet.”

The fix: Use orchestration and specialized components instead of trying to create the perfect mega-prompt. If you’re writing 2000-word prompts with dozens of examples, you need more components, not better prompts.

The Accuracy Obsession

What happens: Team refuses to ship until the system achieves 99%+ accuracy.

Why it fails: They never ship. They don’t learn from real usage. Meanwhile, humans are making errors at a much higher rate than the AI they’re afraid to deploy.

The fix: Compare AI accuracy to human baseline, not to perfection. Ship with human review for low-confidence cases. Improve based on real-world feedback, not synthetic test sets.

The Infrastructure Premature Optimization

What happens: Team spends months building elaborate infrastructure for monitoring, evaluation, and orchestration before they’ve proven the core concept.

Why it fails: They’re optimizing for scale before they’ve achieved product-market fit. The infrastructure becomes technical debt when they realize they need to pivot.

The fix: Start with duct tape and scripts. Build infrastructure incrementally as you prove value. Move fast with prototype code, refactor once you know what you’re building.

The No-Verification Deployment

What happens: Team builds a system that generates outputs without any verification loops. They ship it and discover it fails silently, producing plausible-but-wrong results.

Why it fails: Compound AI systems need verification at multiple stages. Without it, errors compound and you lose user trust immediately.

The fix: Build verification into your architecture from day one. Generated code should be executed and tested. Factual claims should be checked against sources. Numerical answers should be sanity-checked. Start with human verification, automate as you learn common error patterns.

The Organizational Readiness Assessment

Before embarking on compound AI development, assess whether your organization is actually ready. Here are the prerequisites that successful implementations had in place:

Executive sponsorship: Someone in leadership who understands the investment timeframe (months, not weeks) and is willing to champion the project through the learning curve.

Cross-functional collaboration: Compound AI projects require data engineers, ML engineers, product managers, and domain experts working together. Siloed organizations struggle.

Data accessibility: If your data is locked in disconnected systems with inconsistent schemas and no clear ownership, fix that first. Compound AI needs data access.

Tolerance for iteration: Organizations that expect AI projects to work perfectly on the first try will be disappointed. Success requires rapid iteration based on real usage.

Measurement discipline: You need clear metrics for success and the ability to actually measure them. “We’ll know it when we see it” doesn’t work.

One company I advised had enthusiastic leadership support but no data accessibility. Every query required approvals from multiple teams. They wisely paused their compound AI initiative to fix their data infrastructure first. Six months later, they were ready and their implementation moved fast.

The Metrics That Actually Matter

Forget the AI benchmarks. Forget the model leaderboards. When you’re building compound AI systems for real business value, here are the metrics that matter:

Time-to-value: How long until users experience tangible benefit? The best projects deliver value in weeks, not quarters.

Adoption rate: What percentage of the target user base actually uses the system? The most sophisticated AI is worthless if nobody adopts it.

Task completion rate: What percentage of user intents are successfully handled without human intervention? This reveals whether your orchestration works.

User satisfaction: Direct feedback from users. Are they delighted, indifferent, or frustrated? This predicts whether the system becomes part of daily workflow or gets abandoned.

Cost per successful interaction: What does it cost to successfully complete a user task? This combines API costs, infrastructure, and human review time.

Error escalation rate: How often does the system need human intervention? This should decrease over time as you improve orchestration and add verification.

Iteration velocity: How quickly can you ship improvements based on user feedback? This reveals whether your architecture supports learning.

A customer service AI might have mediocre accuracy on benchmarks but achieve 85% task completion with 90% user satisfaction and cost 40% less than human-only support. That’s success, regardless of what the benchmarks say.

Your First 90 Days: A Tactical Roadmap

Ready to start? Here’s what the first 90 days should look like:

Days 1-14: Discovery and Scoping

Interview 10-15 potential users about their biggest pain points
Identify the single most valuable use case (resist the urge to tackle everything)
Define specific success metrics
Assess data availability and access
Identify the 2-3 components you’ll need
Decide build vs. buy for each component

Days 15-30: MVP Development

Build the simplest possible system that delivers value
Focus on core workflow, ignore edge cases
Use managed services wherever possible
Implement basic error handling and human escalation
Create internal demo environment

Days 31-45: Internal Testing

Deploy to 5-10 power users
Collect detailed feedback on every interaction
Identify most common failures
Measure baseline metrics
Iterate rapidly based on feedback

Days 46-60: Refinement

Add verification loops for common error patterns
Improve orchestration based on observed usage
Expand coverage of handled scenarios
Build evaluation framework for ongoing testing
Prepare training materials

Days 61-75: Broader Rollout

Deploy to 30-50 users
Monitor metrics daily
Conduct weekly feedback sessions
Continue rapid iteration
Document learnings and best practices

Days 76-90: Scaling and Planning

Deploy to full target user base
Analyze metrics and ROI
Identify next component or capability to add
Plan roadmap for next quarter
Share learnings with broader organization

This timeline is aggressive but achievable for the right first project. The key is maintaining ruthless focus on delivering value quickly, not building the perfect system.

The Questions to Ask Before You Start

Before committing resources to a compound AI project, pressure-test your plan with these questions:

Can you articulate the value in one sentence? If you can’t explain why this matters in 15 seconds, you don’t have clarity yet.

What’s the manual process this replaces or enhances? If you’re not augmenting something that already works, you’re building on uncertain ground.

How will you measure success? Specific numbers, not vague aspirations.

What’s the failure mode if the AI component gets it wrong? If the answer is “catastrophic,” you need more verification loops before deployment.

Who are your first 10 users and have you talked to them? If not, you’re building in a vacuum.

What’s the 80% solution? If you can’t identify what “good enough to ship” looks like, you’ll never ship.

Do you have the organizational support to iterate? One deployment isn’t success—continuous improvement is.

Looking Forward: Building Your Compound AI Capability

The goal isn’t to build one compound AI system. It’s to develop organizational capability in compound AI development that becomes a sustainable competitive advantage.

Organizations that get this right don’t just ship one project—they build momentum. The second project takes half as long as the first. The third project reuses components from the first two. The team develops intuition for orchestration patterns that work. Evaluation frameworks become reusable. Integration patterns become templates.

Within 12-18 months, these organizations can ideate on a compound AI capability and have it in production within weeks, not months. They’ve built not just systems, but institutional knowledge.

That’s the real prize.

The compound AI revolution isn’t about the technology—it’s about the organizations that learn to wield it effectively. The winners won’t be the ones with the most sophisticated systems on day one. They’ll be the ones who start simple, ship fast, learn continuously, and build capability over time.

The question isn’t whether to build compound AI systems. It’s whether you’ll be ahead of the curve or playing catch-up in 18 months.

The time to start is now. Start small, but start.

What’s your biggest challenge in getting started with compound AI? What’s stopping your organization from taking the first step? Let’s discuss in the comments—I’m genuinely curious about the barriers people are facing.