Autonomous optimization for production AI agents

Converra continuously evolves your agent's prompts—without risking regressions. Changes are simulated offline and gated before anything ships.

$npm install converra
Production Problems

What teams optimize with Converra

If you can measure it, Converra can optimize for it. Here are the most common failure modes teams bring to us:

Drift & Decay

Performance degrades as models update and usage patterns shift

Cost Blowups

Token usage spikes without visibility into what's driving it

Latency Issues

Response times creep up, hurting user experience

Inconsistency

Same inputs produce wildly different outputs across runs

Schema Failures

Structured outputs break downstream systems and integrations

Hallucinations

Ungrounded claims erode trust and create liability

Task completion, CSAT, conversion, safety scores, custom evals—bring your metrics, we run the loop.

The Reality

Why agents don't self-improve by default

Production AI agents are improved through manual, ad-hoc workflows—not through a continuous improvement system. Teams rely on copy-pasted transcripts, one-off tests, and dashboards that stop at observation. As traffic grows, models change, and use cases expand, this approach becomes slow, risky, and impossible to govern.

Build-time prompt frameworks help you find better prompts during development. But once your agent is live, you need a different system—one that continuously improves production behavior while preventing regressions.

Converra is that system.

How Converra closes the production improvement loop

Real Production Interactions

Learn from actual user conversations

Objective-Driven Optimizations

Tied to your business goals

Persona-based Simulation

Test changes without production risk

Governed Rollouts

Deploy safely with instant rollback

What changes

The Problem

Manual agent improvement breaks at scale

Prompt changes rely on copy-pasted transcripts and playground testing.
Each experiment requires bespoke A/B tests, dashboards, and glue code.
Platform and ML teams spend cycles preventing regressions instead of shipping.
Monitoring tools show what broke—but offer no path to improvement.
The Outcome

Better agents, without the human bottleneck

Agents improve from real production interactions—not curated examples.
Variants are auto-generated, simulated, and gated; only validated winners ship.
Rollouts follow explicit objectives—not tribal knowledge.
Changes deploy progressively with instant rollback—no pipeline rewrites.

Monitoring tells you what happened. Converra makes improvement continuous—and safe.

How it works

Connect once, improve continuously

Converra handles the optimization loop autonomously. You set goals and approve what ships.

Your Agent
Converra
Better Agent
Repeat continuously

Connect your data, set a goal

Production interactions flow in via paste, upload, or SDK. Trigger optimization on-demand or automatically with optional objectives.

Converra runs the full loop

autonomous
Analyzes prompt and history
Generates targeted variants
Creates personas and scenarios
Simulates variants head-to-head
Learns and iterates mid-run
Finds winner with confidence

Deploy with confidence, monitor always

Review and approve winners (or enable auto-accept). Converra tracks production performance and alerts you if something drifts.

Guardrails

Ship changes with confidence

Every optimization goes through validation before anything touches your production prompts.

Manual approval before deploy

Review simulation results and approve winners before they're applied. You stay in control of what ships.

Version history & rollback

Every optimization creates a new version. Roll back to any previous version with one click if something doesn't work.

Statistical confidence scoring

Only variants that show real improvement with statistical confidence are recommended. No more guessing if changes helped.

Full optimization history

See every optimization attempt, what was tested, and how variants performed. Full visibility into what changed and why.

Results

Real improvements, measured

Every optimization produces a clear before/after comparison with statistical confidence.

Optimization Results

Head-to-Head Comparison

Baseline72.3%
Winner (Variant B)
89.1%+17pp

All Metrics

Task Completion+17pp
AI Relevancy+12pp
User Sentiment+8pp
Confidence94%
+23%Task completion

Support agent resolved more tickets without escalation after 3 optimization cycles

+18%User sentiment

Onboarding flow conversations became more helpful and engaging through iterative refinement

+15%AI relevancy

Sales assistant responses became more focused and on-topic after optimization

Integrations

Connect your prompts & traffic

Whether you paste data, use our SDK, or connect via MCP—Converra turns your prompts and production data into actionable insights.

"Why is my support agent failing on refund requests?"

Converra analyzes conversation patterns and surfaces the specific failure modes in your prompt.

"Optimize my onboarding prompt for task completion."

Generates variants, runs simulations against realistic personas, and recommends the winner.

"Show me prompts that are underperforming this week."

Tracks performance over time so you catch regressions before users complain.

Fits your workflow

Start no-code, then integrate when you're ready.

View integration docs

Node.js SDK

TypeScript, typed APIs

MCP Server

Claude, Cursor, any MCP client

REST / JSON-RPC

Any language, any stack

Join the beta

We're working with a small group of beta customers and adding teams by invite. If you run production AI agents and want to stop hand-tuning prompts, request access and we'll reach out.

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