How self-improvement works—without regressions

Converra turns your production conversations into prompt improvements you can trust. Changes are simulated offline, gated by confidence, and deployed with versioning and instant rollback.

🤖

Production AI

Live traffic • Never A/B tested

Converra

🔍
Analyze
Generate
🧪
Simulate
🚦
Gate
OfflineNo Regressions

Optimized Prompt

v3

+18% task completion

↺ Deploy (versioned) + rollback

Converra is

  • The performance layer for production AI agents
  • Simulation-first, with gated deployment decisions
  • Compatible with your existing stack (no pipeline rewrite)

Converra is not

  • Only observability (“here's what broke”)
  • A prompt design playground
  • Runtime A/B infrastructure (no prod traffic experiments)
Competition

Head-to-Head Comparison

Converra runs the full optimization loop end-to-end—so improvements are proven before they ship.

Prompt playgrounds / prompt IDEs
Great for: Rapid exploration and prototyping
Breaks down: Hard to prove improvements hold at scale
Converra wins: Tests variants at scale; recommends winners that pass your guardrails
LLM observability / tracing
Great for: Visibility, debugging, and diagnosis (cost/latency/failures)
Breaks down: Doesn't produce changes; still requires manual iteration
Converra wins: Turns insights into validated changes—automatically
Evaluation suites
Great for: Measurement discipline (datasets, scorers, comparisons over time)
Breaks down: No variant management, selection, or deploy workflow
Converra wins: Full cycle: variants + winner selection + versioned deploy/rollback
Runtime A/B / experimentation platforms
Great for: Live traffic splits for product and UI metrics
Breaks down: Risky for agent changes; regressions found in prod
Converra wins: Validates offline before any production exposure
DIY (spreadsheets + transcript review)
Great for: Early-stage learning and quick iteration
Breaks down: Subjective, doesn't scale, knowledge lives in people
Converra wins: Versioned, auditable, one-click deploy/rollback

Fit, stated plainly

Choose Converra when you have a human-facing agent in production and want measurable improvement without regressions—without building a custom optimization pipeline.

Keep your observability/evals tools: Converra can sit on top of your existing stack and turn measurement into validated change.

If you're still defining what the agent should do (early discovery, low stakes), playgrounds/DIY may be enough—until repeatability and risk control matter.

Best together: Converra doesn't replace your tracing or evals—it uses them as inputs and runs the optimization loop end-to-end. Keep your observability for visibility, your eval suites for measurement, and let Converra handle the analyze → generate → simulate → select → deploy cycle.

The optimization loop, step by step

From connection to continuous improvement

1

Connect once

Your data, your way

Add the Converra MCP to your AI coding assistant and let it handle the rest. Or connect LangSmith for continuous sync, use our SDK/API, or paste transcripts directly.

  • MCP: works with Cursor, Claude Code, Windsurf, and any MCP client
  • LangSmith: continuous sync (hourly → daily) with org-scoped API keys
  • SDK/API: integrate programmatically from your backend
2

Discover agent systems

See the full picture

Converra automatically groups multi-agent traces into agent systems, visualizes execution paths, and identifies the weakest link in each chain.

  • Multi-agent traces grouped by parent-child relationships
  • Path visualization shows how agents chain together
  • Weakest-link scoring surfaces where to optimize first
3

Generate targeted variants

Not random rewrites

Converra generates a small set of candidate prompt variants (typically 3–5). Each variant targets specific improvements while preserving constraints.

  • Changes are structured to be explainable
  • Constraints preserved (schema, boundaries, brand voice)
  • Think "small, provable edits" not "prompt roulette"
4

Simulate head-to-head

key

The core differentiator

Each variant is tested against personas that represent real user types and scenarios derived from production patterns—including edge cases. Simulations are run head-to-head against the baseline.

  • Personas: new, frustrated, technical, power user
  • Multi-turn conversations, not just single-turn
  • Path-aware simulations for multi-agent systems
  • Exploratory mode (faster) or Validation mode (higher confidence)
5

Regression test

Automatic safety check

When a variant shows improvement, Converra automatically tests it against a "golden set" of scenarios your baseline handles well. Regressions are caught before deployment—you see the tradeoff and decide.

  • Golden sets auto-generated from prompt analysis
  • Short 2-3 turn exchanges for fast validation
  • Fluke detection prevents false positives
  • Regressions surfaced, not auto-blocked—you decide
6

Gate and ship

Confident deployment

Variants are evaluated across improvement metrics and regression results. Deploy winners with versioning and instant rollback. Manual approval by default, or auto-accept for trusted prompts.

  • Task completion, response quality, sentiment
  • Safety/policy adherence, schema compliance
  • Version history with one-click rollback
  • Webhook notifications for CI/CD integration

Always-live optimization

Converra doesn't stop after one win. When production performance drifts—models update, user behavior shifts, new edge cases appear—Converra can alert you, auto-trigger new optimizations, and keep your prompts improving without constant engineering cycles.

The loop compounds. Each improvement becomes the new baseline.

FAQ

Do I need a dataset?

No. Converra can generate test coverage from personas and scenarios derived from production patterns. You can still use real conversations as grounding input.

Will Converra break what's working?

That's what simulation + gating is designed to prevent. Improvements must prove lift and avoid regressions before shipping.

How long does an optimization take?

Exploratory runs in 5–15 minutes; validation runs take 30–60 minutes for higher confidence.

Can I bring custom personas, metrics, or rules?

Yes—you can tailor what Converra tests and what it optimizes for.

Ready to stop hand-tuning prompts?

Let Converra handle the optimization loop while you focus on building your product.