CI for AI agents.
Turn expert feedback into verified pull requests.
Observe production behavior, evaluate outputs with domain experts, and ship safe improvements through PRs you can review, test, and approve.
Free during early access · No credit card required
Refined routing prompt to prioritize billing questions. Quality improved on the evaluation suite without changing production directly.
The problem
You shipped an agent.
Improving it shouldn't be this hard.
Developers build agents using best guesses about how they should behave. Domain experts test them and provide feedback — but that feedback rarely translates cleanly into improvement. Instead, teams fall into a slow loop of interpretation, rework, and trial-and-error.
Agent quality doesn't stall because models are weak. It stalls because the improvement process is broken.
Agent development still lacks a reliable improvement loop. Agolvia exists to close that gap.
Introducing
Continuous Agent Improvement
Developers build capability. Experts evaluate behavior. Agolvia turns feedback into verified improvements — no translation required.
Slow, lossy, and expensive. The developer becomes a translator.
Experts improve agents directly. Developers keep control.
How it works
A repeatable loop for agent quality
Turn expert judgment into measurable evaluations—and verified PRs your team controls.
Connect your repo
Agolvia scans your codebase to identify agents, prompts, models, tools, and orchestration topology—so you can see what's running and where to improve it.
Add tracing safely
Agolvia opens a PR to add tracing instrumentation. Once merged, you can observe inputs, outputs, tool usage, and performance patterns to establish behavioral baselines.
Capture expert judgment
Domain experts score outputs, flag risks, and describe the preferred behavior. Their feedback becomes structured, reusable evaluation data.
Ship verified improvements
Agolvia proposes improvements—prompts, models, tools, workflows—validated against your evaluation suite. Changes arrive as reviewable pull requests. Nothing ships without approval.
Capabilities
Make agent quality an engineering practice
The workflow you'd expect if correctness and safety were treated like first‑class engineering concerns.
See what's running
Inventory every agent, prompt, and model. Understand how they're connected—and where the leverage for improvement is.
Evaluate before production
Test prompt changes, model swaps, and workflow updates against your evaluation suite before anything reaches customers.
Pull requests only
Every change arrives as a PR. Your team reviews and approves—no black‑box edits to production behavior.
Expert-driven quality
Domain experts evaluate outputs directly—no code changes required. Their judgment becomes part of the improvement system.
Works with your stack
Connect to what you've already built—LangChain, CrewAI, or custom systems. No framework migration required.
Regression detection
Continuous evaluation catches quality regressions before users do. Know when behavior drifts from established baselines.
Built for
Where engineers and experts align on "correct"
Agolvia bridges the gap between the teams who build agents and the people who can judge their outputs—so quality improves faster.
AI Engineers
See production behavior, run evaluations, and ship improvements with evidence—through PRs you control.
Platform Engineers
Standardize tracing and evaluation across agents. Catch regressions early and keep quality visible across teams.
Domain Experts
Evaluate outputs with your expertise—law, finance, compliance, support. Guide improvements without touching code.
Technical Founders
Ship reliable AI systems with less uncertainty—backed by evaluation results, not gut feel.
Before & after
From translation bottleneck to improvement loop
Use cases
Built for teams that can't "hope it works"
Where correctness, safety, and reliability need a real improvement loop.
Lawyers evaluate contract review outputs. Their corrections become structured evaluations that validate prompt improvements before they ship.
Compliance teams review agent-generated reports. Evaluations feed improvement cycles and surface regressions early.
Support leads score responses for accuracy and tone. Routing changes are evaluated against a ticket-based suite before deployment.
Product teams evaluate internal copilots. Domain-specific evaluation suites keep agents improving on the tasks that matter.
Philosophy
Improvement you can trust
Agolvia was designed around a simple belief: improving agents should feel like engineering, not guesswork.
Safe by design
No production behavior changes automatically. Every improvement is proposed, reviewed, and approved by your team.
Repository-native
Agolvia works in your production repository. Improvements arrive as pull requests—transparent, auditable, and reversible.
Human + machine collaboration
Experts guide improvement without modifying code. Their domain knowledge becomes evaluation intelligence that compounds over time.
Improve agents with CI discipline.
Agolvia is in private early access for teams running production agents. Bring expert-driven evaluation and PR-based improvement to your workflow.