reliability layer for production agents

The agents that pass your evaluations are
still failing.

Sentric operates the continual learning loop for production AI agents. We build the verifiers your evaluation is missing, ship measurable improvement against them, and run the loop continuously as your data and models change.

PROMPTTRAINHOLDVERIFIERFAILURE MODESATTRIBUTIONACTION ROUTERVERIFIER ENGINEDEPLOYHOLDROLLBACKPRODUCTION WORKFLOWSAGENT INTERACTIONSTOOL CALLSIMPROVEMENTCONTROLLED DEPLOYMENTCONTINUOUS MONITORINGSentric Reliability Loop
Problem

Capability is solved. Reliability isn't.

Foundation models will keep getting better. Capability is no longer what determines whether an agent succeeds in production. The constraint is the layer above the model — whether the agent operates dependably on your workflow, your data, your edge cases. Generic agents don't know your reality. And the metrics used to measure them reward correct-looking outcomes rather than correct behavior — train against that signal, and the model learns to reach the correct-looking outcome more reliably, not to solve the underlying problem. Neither failure can be closed by a stronger general model. Every team running production agents at scale discovers this. Most discover it after a customer-visible failure forces them to look.

Solution

The reliability layer that becomes your proprietary advantage.

Sentric runs the continual learning loop on one or more of your production workflows. Verifiers grounded in adversarial-ML methodology catch the failure patterns outcome metrics reward by accident. Improvement cycles ship against the stricter signal, including custom models trained on your workflow's data. The loop operates continuously as your data, your users, and your underlying models change. Everything it produces — verifier rubrics, failure-mode taxonomy, trained checkpoints, operational knowledge of your agent's behavior — accumulates inside your environment as proprietary intelligence that exists nowhere else. The reliability layer that lets agents stay dependable as they scale, the way observability became required infrastructure for distributed systems.

Platform

What we run inside your environment.

A four-part system. Operated as one continuous engagement, with trained models delivered to your environment.

01Verifier engine

Workflow-specific verifiers, designed using adversarial-ML methodology, score every production trajectory against rubrics calibrated to your standards. Catches the failure patterns outcome metrics reward by accident — agents passing tasks while bypassing authentication, mutating records without confirmation, deciding from incomplete data. The source of truth the rest of the system runs against.

02Failure-mode discovery

Failed and gamed trajectories cluster into named patterns specific to your workflow. The taxonomy expands continuously as your data shifts and your users surface new edge cases. Patterns that recur across customers become part of the pattern library, so each new workflow starts further along the discovery curve than the last.

03Improvement loop

Each cycle filters trajectories against the verifier and applies the cheapest training intervention that moves the metric — prompt and program changes, supervised fine-tuning, preference optimization, custom models trained on your workflow's data. Every change ships through canary deployment with automatic rollback on regression.

04Continuous operation

The loop runs as your data drifts, your users change behavior, and your underlying models update. Drift, base model changes, and regression on previously-fixed patterns are caught and addressed without your team intervening. The agent stays at the quality bar you set, and the bar can keep rising.

01

Verifier engine

Workflow-specific verifiers, designed using adversarial-ML methodology, score every production trajectory against rubrics calibrated to your standards. Catches the failure patterns outcome metrics reward by accident — agents passing tasks while bypassing authentication, mutating records without confirmation, deciding from incomplete data. The source of truth the rest of the system runs against.

Sentric is an applied AI lab operating the continuous improvement system for production agent workflows.

ACCESS

Request a deployment.

Private beta. Limited engagements.

We're prioritizing teams running tool-using agents in production at scale, with clear ownership of the workflow and budget for embedded engineering.

We respond within 48 hours. Happy to talk through fit before any commitment.