Move agentic systems from experimentation to production with controls for ownership, tool access, data use, quality, and rollback.

AI agents are probabilistic systems operating inside deterministic businesses. They may interpret instructions differently, select tools dynamically, and expose sensitive context through external services. Traditional CI/CD remains necessary, but it is not sufficient by itself.
We create an agent delivery lifecycle that makes behavior testable and authority explicit.
Catalog agents, models, prompts, tool servers, APIs, datasets, and deployment environments. Assign accountable owners and workload identities so actions can be traced to a specific system and release.
Build representative evaluation datasets and automated checks for task quality, hallucination, prompt injection, sensitive-data leakage, unsafe tool use, and regression. Promotion criteria are explicit and versioned.
Scope each agent’s tools, data, network destinations, and allowable actions. OPA policies evaluate identity, environment, requested operation, and risk before access or deployment is allowed.
Trace prompts, tool calls, policy decisions, and outcomes without collecting unnecessary sensitive data. Use staged rollout, runtime configuration, and tested rollback paths to limit impact.
We can implement the framework with your existing AI and delivery platforms or evaluate capabilities from vendors such as Harness where they fit. Architecture and control requirements lead; vendor selection follows.