AI engineers
Catch agent drift before customers do. One platform to configure, benchmark, release, observe, and automatically improve agents in production.
Configure
Manage prompts, models, tools, evals, and agent workflows from one place, from pre-production through production, with no redeploys needed.
Prompts, models, parameters, and tools are typically defined in code, scattered across repos, and rebuilt by different teams with no shared standard. AgentControl centralizes each agent’s configuration into a single place: versioned, auditable, and accessible to teams across your organization.
Updating a prompt, model, or parameter takes effect in under 200ms, without a code change, PR, or release cycle. The SDK works with leading models and frameworks.
At scale, the risk isn't just one agent behaving unexpectedly: it's dozens of teams defining agent behavior independently with no visibility into what the others are doing. AgentControl gives platform and engineering teams RBAC, audit trails, and change management across their agent configurations.
The LLM playground lets you test prompt and model variants side by side against the same inputs. See how each one performs before committing to a change, without waiting on a deploy cycle.
Upload curated test cases and golden datasets that reflect your real production inputs. Run batch evaluations across your variants to catch regressions, edge cases, and quality gaps before customers encounter them.
Built-in judges cover common quality criteria like accuracy, coherence, relevance, and safety. Custom judges let you define what quality means for your system. Every run produces a consistent, repeatable score so regressions surface before they ship.
Run offline evals, A/B tests, and agent optimization tools before any agent change reaches live traffic.
Target and gradually roll out to limit exposure, then enforce guardrails that catch issues and automatically remediate in real time before customers are impacted.
Not every user needs the same agent version. Targeting lets you route specific configurations to specific segments: a cost-efficient model for commercial customers, a more capable one for enterprise, a regionally compliant one for specific markets. Changes take effect in real time.
Guarded rollouts let you ship new agent versions to a percentage of traffic first. Define the signal thresholds that trigger action: online eval scores, cost, latency, and negative feedback. AgentControl expands the rollout as they hold, and pulls back automatically if they regress.
Adaptive Triggers monitor responses against thresholds you define. For example, when an error rate is exceeded on a model from provider A (whether due to rate-limiting or availability incident), AgentControl automatically directs your agent to use a model from provider B, before your customer experience is impacted.
Observe
Traces and online evals give you the full picture. When behavior shifts, you know exactly what caused it.
Traces show what each agent received, produced, and how long each step took. AI Insights tracks how prompt, model, and parameter changes move key metrics over time, connecting the dots when something shifts.
Agent Graphs display per-node performance metrics (latency, invocations, tool calls) directly in a workflow diagram. When something drifts, you can update a configuration or adjust targeting from the same view, without a redeploy.
Agents can drift without warning. A model gets updated, a prompt produces unexpected results at scale, or an edge case surfaces that evaluations missed. Online evals score live responses continuously, so drift detection flags the change early enough to act.
Iterate
Run A/B tests on live traffic. Let production data determine which configurations perform better.
Define multiple variants of a prompt, model, or configuration and run controlled experiments. Use A/B, A/A, and multi-armed bandits to compare variants by LLM-as-a-judge scores, cost, latency, user feedback, task completion, and business outcomes, while continuously shifting traffic toward the best performers.
Multi-armed bandit experiments go further than A/B tests. Rather than waiting for an experiment to conclude before acting on results, they continuously shift traffic toward the better-performing variant, optimizing in real time while the experiment is still running.
LLM judge scores tell you about output quality—not whether a better response improved resolution rate, reduced cost per conversation, or moved customer satisfaction. Measure experiment results against the business metrics that matter, so configuration decisions are grounded in outcomes.
We can take bigger risks in the kinds of AI features we build, and we can validate that they’re worth it because we can see downstream impacts all the way through.
Configure, evaluate, release, observe, and improve agent behavior from one control plane, without touching the code that runs it.
