SOSA™ is the four-pillar governance framework behind every OpsAgents deployment. It reconciles agent autonomy with organizational accountability — so autonomous AI can run on real business data and still be trusted, audited, and controlled.
A formal framework for deploying autonomous AI agents in enterprise operations — without trading away supervision, security, or auditability.
Most agent frameworks treat autonomy and control as opposing forces — ending up either too brittle to scale or too opaque to trust. SOSA™ — Supervised Orchestrated Secured Agents is a four-pillar methodology that reconciles agent autonomy with organizational accountability, and OpsAgents is its production reference implementation across multiple business verticals. Every pillar is a load-bearing engineering decision, not a marketing slogan — backed by graduated human-in-the-loop checkpoints, a real orchestration layer, zero-trust isolation, and a recorded audit trail.
Each pillar answers one of the four questions every enterprise asks before it puts an AI agent in production.
Human-in-the-loop checkpoints are first-class primitives, not add-ons. Supervision is graduated: routine, low-risk tasks run autonomously, while high-stakes actions — financial transactions, external communications, anything irreversible — require explicit human approval before they execute.
Daily briefings surface every action taken, and an agent that consistently meets its success criteria earns wider autonomy over time; one that drifts is escalated back to tighter supervision.
An orchestration layer coordinates agents across time, context, and toolchains. Agents share structured context and run on business-logic DAGs — not ad-hoc cron triggers or brittle message passing.
A centralized scheduler sequences agents across their temporal and data dependencies, so the right agent acts on the right context at the right moment, with no two agents stepping on the same outcome.
Security is a property of every layer, not a perimeter. Each agent runs isolated, with scoped credentials, zero-trust boundaries, and verifiable audit trails — no access beyond its declared permission set.
Per-client isolated deployments are available, with scoped OAuth credentials; business data is retained only as long as the workflow needs it (see our Privacy Policy), and every single action is written to an audit log before it leaves the system.
Not scripts with LLM wrappers — goal-directed entities with persistent context, real tool use, and adaptive planning. Each agent has a defined role, explicit success metrics, and a failure-recovery path.
They reach real systems — calendars, CRMs, accounting, recruiting pipelines — and own a business outcome end-to-end: reasoning, deciding, and acting, then verifying the result against their success criteria.
SOSA™ isn’t a diagram — every run writes an audit trail. Below is a real, unedited slice recorded from one production worker run (service opsagent-recruiter, 2026-06-07), tenant identities redacted and everything else verbatim: action types, decisions, timestamps, and the governance gates that blocked ungoverned sends before they left the system. The two highlighted rows are those gates in action.
Every agent runs a three-phase loop — Plan, Act, Verify — with each action executed against real systems and recorded in an audit log. Agents that consistently meet their success criteria earn wider autonomy; those that drift are escalated to tighter supervision. The result is a trust gradient that addresses the three barriers to enterprise adoption of autonomous AI: accountability, reliability, and compliance.
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Scoped access, approval gates and review before release are part of how the system is built. Autonomy is earned inside them.
Then the loop runs again. Meet the criteria and the approval boundary widens. Drift, and the agent goes back to tighter supervision.
The trust gradient: where the approval boundary sits
Autonomy is earned. When an agent keeps meeting its success criteria, its approval boundary widens. When it drifts, the boundary tightens and supervision returns.
The dark marker is the approval boundary. This is an illustration of the method; it shows no live scores or readings.
OpsAgents is the first commercial implementation of the SOSA framework. Every architectural decision — from isolated environments to the orchestration scheduler to the human-approval gates — maps directly to a SOSA pillar.
Configurable approval workflows per agent. Daily briefings surface all actions taken. High-impact operations (financial transactions, external communications) require explicit human sign-off.
A centralized scheduler coordinates agents across temporal and data dependencies. Agents share context through structured registries, not ad-hoc message passing.
Per-client isolated deployments are available, with scoped OAuth credentials. Business data is retained only as long as the workflow needs it. Every action is logged to an audit trail.
Goal-directed agents with persistent memory, real tool access (calendars, CRMs, accounting systems), and adaptive planning — not scripts, not wrappers, not demos.
The complete methodology, the execution model, and the production architecture — written up as a formal framework paper from OpsAgents AI Research.
What SOSA is, why it matters, and how to verify it in production.
SOSA stands for Supervised Orchestrated Secured Agents — the governance framework behind OpsAgents. It keeps every agent action supervised, orchestrated across agents, security-bounded, and auditable, which is what makes autonomous AI agents safe to run on real business data.
Most agent frameworks force a choice between autonomy and control — ending up too brittle to scale or too opaque to trust. SOSA reconciles agent autonomy with organizational accountability through four pillars: Supervised, Orchestrated, Secured, and Agents, each backed by graduated supervision, an orchestration layer, zero-trust isolation, and an audit trail.
Every agent action is written to an audit log. This page embeds a real, unedited slice of the SOSA audit trail from one production worker run — tenant identities redacted, decisions, timestamps, and governance gates verbatim. You can also read the full SOSA white paper or book a SOSA security review.
Bring SOSA™ to your operations. Book a SOSA security review and we’ll walk your team through the supervision gates, the isolation model, and the audit trail — against your real workflows.
Book a SOSA Security Review →