Your agents ship code.
Who ships improvements to them?
Agolvia brings CI/CD discipline to AI agents. Observe behaviour, evaluate outputs with domain experts, and ship verified improvements — all through pull requests.
Free during early access · No credit card required
Refined routing prompt to prioritise billing queries. Accuracy improved from 91.1% to 94.2% across 847 test cases.
The problem
You built the agent.
Then what?
Most teams deploy agents and hope for the best. Prompts drift. Outputs degrade. Experts with the knowledge to fix things can't access the levers. The result? Agents that launch well and slowly get worse.
Agent development lacks the equivalent of automated testing, code review, and CI/CD. Agolvia exists to close that gap.
How it works
Four steps to agents that improve continuously
A structured loop that turns expert feedback into measurable, verified improvements.
Connect & discover
Point Agolvia at your repository. It identifies agents, prompts, models, tools, and orchestration logic — giving you a complete map of how intelligence operates inside your system.
Observe & trace
Agolvia opens a PR that adds tracing instrumentation. Once merged, you can observe inputs, outputs, reasoning steps, tool usage, and performance patterns to establish behavioural baselines.
Evaluate with experts
Domain experts — lawyers, analysts, support leads — compare outputs, score quality, flag risks, and describe preferred behaviour. Their judgment becomes reusable evaluation intelligence.
Improve through PRs
Agolvia proposes improvements — to prompts, models, tool usage, or workflow structure — validated against your evaluation suite. Changes arrive as reviewable pull requests. Nothing ships without your approval.
Capabilities
Engineering discipline for AI agents
The tools you'd expect if agent quality were treated as a real engineering problem.
Full agent visibility
See every agent, prompt, and model in your system. Understand what's running, how it's connected, and where the levers for improvement are.
Controlled experiments
Test prompt changes, model swaps, and workflow adjustments against your evaluation suite before anything reaches production.
PR-based changes only
Every improvement arrives as a pull request. Your team reviews, tests, and approves. No black-box modifications to production behaviour.
Expert-in-the-loop
Domain specialists evaluate outputs directly — no code changes required. Their judgment feeds back into systematic improvement.
Framework agnostic
Works with your existing agent stack. LangChain, CrewAI, custom implementations — Agolvia connects to what you've already built.
Regression detection
Continuous evaluation catches quality regressions before your users do. Know when behaviour drifts from established baselines.
Built for
Where engineers and experts meet
Agolvia bridges the gap between the teams who build agents and the people who know whether they're working.
AI Engineers
Understand how agents behave in production. Run controlled experiments. Ship improvements with confidence through PRs you control.
Platform Engineers
Get observability across the agent topology. Detect regressions early. Maintain quality standards without manual oversight.
Domain Experts
Evaluate agent outputs using your expertise — in law, finance, compliance, research. Guide improvements without touching code.
Technical Founders
Reduce the uncertainty of deploying intelligence. Ship higher-quality AI systems faster, with evidence that they're improving.
Before & after
From chaotic experiments to engineering discipline
Use cases
Built for high-stakes domains
Where agent quality isn't optional — it's a requirement.
Lawyers evaluate contract review outputs. Agolvia turns their corrections into prompt improvements — verified against thousands of past contracts.
Compliance teams review agent-generated reports. Quality scores feed continuous improvement cycles, catching regressions before regulators do.
Support leads score agent responses for accuracy and tone. Routing improvements are tested against historical tickets before deployment.
Product teams evaluate internal copilots. Domain-specific evaluation suites ensure agents get better at the tasks that matter most.
Philosophy
Improvement you can trust
Agolvia was designed around a simple belief: optimising intelligence should feel like engineering, not experimentation.
Safe by design
No production behaviour changes automatically. Every improvement is proposed, reviewed, and approved by your team.
Repository-native
Agolvia works directly in your production repository. Improvements are 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.
Stop guessing. Start improving.
Agolvia is currently in private early access. Request access to bring continuous improvement to your production AI agents.