Versioned runtime for AI systems

Turn prompts, tools, and workflows into versioned runtime components you can inspect, compare, and improve — without scattering AI logic across your codebase.

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Building got cheaper. Product truth didn't.

AI has reduced the cost of building software by an order of magnitude.

Now the bottleneck is no longer shipping features — it's knowing what to build, what's working, and how to iterate toward value with confidence.

Most teams can generate code and wire models quickly. But once AI behavior reaches production, things get harder:

  • prompts spread across services and handlers
  • tool logic disappears into application code
  • behavior changes without a clear record of why
  • failures are difficult to reproduce
  • teams can ship endlessly without proving they're converging on the right thing

You end up with AI features in production, but no reliable system for understanding or improving them.

What breaks in production

  • prompts embedded as strings across multiple services
  • tool execution hidden inside request handlers
  • no clear record of what happened in a run
  • no safe way to compare behavior across versions
  • model and workflow changes that are hard to trace over time

Treat AI behavior as a system, not scattered logic

Prompt Orchestra gives you a structured runtime for AI systems.

Prompts become reusable, versioned components — not strings buried in code. Test them, pin them in production, and roll them back when behavior changes.

Skills combine prompts, tools, model defaults, and runtime settings into executable units. Context, instructions, model choice, and tool access live in one versioned object.

Agents orchestrate multi-step workflows on top of skills. Break complex behavior into inspectable steps instead of hiding everything inside one oversized prompt.

Runs record execution end to end — inputs, prompt composition, tool calls, outputs, latency, tokens, and cost.

This gives teams a way to move from intuition-driven AI development to structured iteration.

Everything versioned. Everything inspectable. Everything comparable.

  • version prompts, tools, and runtime behavior
  • compose workflows into units you can test and debug separately
  • inspect every run across prompts, tool calls, latency, tokens, and cost
  • compare iterations and see what actually changed
  • reproduce failures with pinned versions and full execution context

Make changes you can reason about

LLMs are probabilistic. Production systems still need to be legible.

Prompt Orchestra does not promise deterministic model outputs. It gives you a reproducible execution context around them:

  • pinned prompt versions
  • explicit runtime configuration
  • traceable tool usage
  • inspectable execution history
  • measurable deltas between versions

So when behavior changes, you can see:

  • what changed
  • where it changed
  • what it cost
  • whether it improved outcomes

That's what makes AI systems maintainable.

Run everything through a clean API

Your application calls the runtime. The runtime handles prompt composition, tool execution, model configuration, and run logging.

POST /api/skills/:id/run

Explicit inputs. Observable outputs. Versioned, traceable execution.

Scoped API keys let you automate allowlisted stateless reads and authoring workflows: update skills, configure tools, manage stateless agent iteration, inspect runs, and execute evaluation batches. Public execution routes remain access-token-only. They fit naturally into CI, staging, internal tooling, and production backends.

Built for operational teams

  • clean HTTP API — no SDK lock-in
  • scoped API keys for backend automation
  • works across CI, staging, and production
  • full execution visibility at every layer
  • version-aware workflows you can manage without redeploying app code

From shipping AI features to learning from them

Most AI tooling helps you build faster.

The harder problem is what happens after deployment:

  • prompts need to evolve
  • tools fail
  • costs drift
  • workflows branch
  • teams need to know whether changes are actually improving the product

Prompt Orchestra is the layer that makes that iteration process structured.

It helps teams move from "something is live" to "we know what changed, what happened, and whether we're closer to delivering value."

That matters when the market rewards not just speed, but the ability to converge on the right product.

Not just a framework. A runtime for iteration.

Frameworks help you wire AI into software.

Prompt Orchestra helps you operate AI behavior over time.

Use it to:

  • inspect production behavior without adding custom logging everywhere
  • reproduce failures with pinned prompts, tools, and config
  • evolve AI behavior without hardcoding it into the app
  • compare versions and learn from real usage
  • treat AI systems like real software infrastructure