What if your AI agents
could self-improve?

The performance layer for production AI agents. Quality, speed, cost—getting better every day.

$npm install converra

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:

Goal Completion

The metric that matters: did the user succeed?

Hallucinations

Ungrounded claims erode trust and create liability

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

Schema Failures

Structured outputs break downstream systems and integrations

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

See all use cases
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.

This isn't a tooling gap—it's a missing layer.

Converra is the performance layer for production AI agents.

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.

The layer your AI stack is missing

Your stack has observability. It's missing a performance layer. Converra sits alongside your runtime—improving agents continuously without touching the request path.

Users
Apps / Agents
LLMs
Response
Observability / Logsprompts, transcripts, metricsUses the logs and transcripts you already capture
reads
writes
ConverraAcross apps, agents & LLMs
Async — zero latency added

Observability tells you what broke. Converra ships the fix—with evidence.

Diagram showing Converra as an optimization control plane alongside the production AI data plane. Prompts and interactions flow from production to Converra, which returns optimized prompt versions for deployment.

Competition

How Converra Compares

Most teams can see what's happening. Few can ship fixes with evidence. Converra closes the loop—turning insights into validated, deployed improvements.

Approach
What it does
What Converra adds
Prompt playgrounds
Explore and prototype on hand-picked examples
Prove what works across real-world variation
Observability / tracing
See what happened (cost, latency, failures)
Turn insights into validated, deployable changes
Eval suites
Measure and score changes consistently
Full cycle: analyze → generate → test → deploy
Runtime A/B testing
Split live traffic to measure impact
Validate offline before any production exposure

Most tools improve visibility. Converra improves the rate you can safely change production behavior.

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.

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

Request Access

Limited spots available. Hands-on onboarding, direct access to the team, and early pricing locked in. If you run production AI agents, apply below.

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