Converra continuously evolves your agent's prompts—without risking regressions. Changes are simulated offline and gated before anything ships.
Performance Lift
Variant 3: 70% vs Baseline: 53%
If you can measure it, Converra can optimize for it. Here are the most common failure modes teams bring to us:
Performance degrades as models update and usage patterns shift
Token usage spikes without visibility into what's driving it
Response times creep up, hurting user experience
Same inputs produce wildly different outputs across runs
Structured outputs break downstream systems and integrations
Ungrounded claims erode trust and create liability
Task completion, CSAT, conversion, safety scores, custom evals—bring your metrics, we run the loop.
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.
Learn from actual user conversations
Tied to your business goals
Test changes without production risk
Deploy safely with instant rollback
Monitoring tells you what happened. Converra makes improvement continuous—and safe.
Converra handles the optimization loop autonomously. You set goals and approve what ships.
Production interactions flow in via paste, upload, or SDK. Trigger optimization on-demand or automatically with optional objectives.
Review and approve winners (or enable auto-accept). Converra tracks production performance and alerts you if something drifts.
Converra treats optimization as a governed system: simulations run under explicit checks for quality, cost, latency, and risk before anything reaches production.
Approval gates & auto-accept
Review and approve winners manually or enable governed auto-accept for low-risk changes.
Progressive rollout & cohorting
Roll out by cohorts or segments so higher-risk variants never jump straight to 100% of traffic.
Instant rollback
Revert to the last safe configuration without touching your orchestration or deployment pipeline.
Evaluation thresholds
Configure minimum lift and confidence criteria so only variants that clear the bar are eligible to ship.
Cost & latency regressions
Detect when a “better” variant is too slow or too expensive relative to baseline, before rollout.
Change history & audit trail
Keep a record of who approved what, when it shipped, and how it performed across key metrics.
Every optimization produces a clear before/after comparison with statistical confidence.
Support agent resolved more tickets without escalation after 3 optimization cycles
Onboarding flow maintained quality while cutting token usage through prompt refinement
Sales assistant response time improved without sacrificing answer quality
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.
Start no-code, then integrate when you're ready.
Node.js SDK
TypeScript, typed APIs
MCP Server
Claude, Cursor, any MCP client
REST / JSON-RPC
Any language, any stack
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.
Already have access? Login here