OpsAgent replaces your back-office with AI agents that actually work. Lead management, sales outreach, recruiting, finance, and content — all automated, all measured by outcomes.
Service businesses spend 40–60% of revenue on operations that don't generate revenue.
Hiring ops managers, bookkeepers, recruiters, and marketing staff costs $250K+/year. Most tasks are repetitive and rule-based.
Manual lead follow-ups fall through the cracks. Invoices don't match. Candidate screening takes days instead of minutes.
You're paying for 10+ SaaS tools that don't talk to each other. The $285B SaaSpocalypse proved this model is broken.
Every agent runs in production — not a demo, not a prototype. Battle-tested on a real business.
AI scans your pipeline daily, prioritizes leads, triggers follow-ups, and delivers morning briefings.
Personalized LinkedIn outreach at scale. Segment, message, track responses — all hands-free.
Full-cycle recruiting — from job posting to candidate submission — handled end-to-end by AI.
From quote to invoice — quotes, orders, receipts, invoices, and inventory managed end-to-end, automatically.
Daily posts across LinkedIn, Instagram, Facebook, and X. On-brand, on-schedule, on autopilot.
Status reports, internal comms, document generation — everything that keeps your company running.
Continuous monitoring of your websites and client sites — uptime, performance, and broken links detected automatically.
Track milestones, deadlines, and deliverables across all client projects — with automatic status updates and blocker alerts.
Full visibility into every client relationship — history, open items, upcoming renewals, and satisfaction signals, all in one place.
Organize, tag, and retrieve brand assets, design files, and media — automatically filed by client, campaign, or project.
Finds new leads that match your ideal client profile — researches companies, enriches contact data, and queues them for outreach.
Generates professional proposals, tracks budget vs. actuals per project, and flags scope creep before it becomes a problem.
Monitors hours in Toggl, tracks team utilization, surfaces capacity gaps, and generates billing-ready time reports automatically.
Triages your inbox, drafts replies, schedules meetings, and keeps your calendar clean — so you start every day organized.
Stores and organizes all company knowledge, client documents, and code per client — searchable, structured, and always up to date.
Automated code review on every PR — catches bugs, enforces standards, and flags issues before they reach production.
Scans your codebase for vulnerabilities, exposed secrets, and dependency risks — continuously and automatically.
Monitors your digital assets across platforms — detects broken links, expired content, and missing files before clients notice.
Custom workflow automations that connect your tools and eliminate repetitive tasks — built, monitored, and maintained by AI.
Monitors access permissions, detects unauthorized changes, and flags suspicious activity across all digital assets and file storage.
Turns raw data from all your tools into clear daily, weekly, and monthly reports — delivered automatically, no manual work needed.
WhatsApp, iMessage, Slack, Monday.com, Trello, Zoho Cliq, Gmail, Google Calendar, Notion, LinkedIn, Toggl, and many more — all connected securely.
We connect to your existing tools and deploy AI agents that start delivering results immediately.
We map your operations in 30 minutes. Which departments are costing you the most? Where do tasks fall through the cracks?
We connect to your email, calendar, CRM, and accounting tools via secure OAuth. AI agents go live within days.
Leads managed. Invoices matched. Content published. You get a daily briefing of everything your AI team accomplished.
OpsAgent isn't theory — it runs the entire operations of a real tech company.
We built OpsAgent to run our own company. Every agent you see here has been running in production for months, handling real leads, real invoices, real recruiting, and real content across multiple platforms.
The result? A full operations stack running with AI — proven, tested, and ready to deploy for your business.
See the Full Case Study →Supervised Orchestrated Secured Agents — a formal framework for deploying autonomous AI agents in enterprise operations.
The rapid proliferation of large language model (LLM)-based agents in enterprise workflows has exposed a critical gap: the absence of a unifying framework that governs how autonomous agents should be supervised, coordinated, and constrained in production environments. Existing approaches treat autonomy and control as opposing forces, resulting in systems that are either too brittle to scale or too opaque to trust. We propose SOSA™ — Supervised Orchestrated Secured Agents — a four-pillar methodology that reconciles agent autonomy with organizational accountability. SOSA provides a formal structure for deploying multi-agent systems that operate continuously, adapt to heterogeneous toolchains, and maintain verifiable compliance with security and governance policies. We further present OpsAgent as a reference implementation of the SOSA framework deployed in production across multiple business verticals.
Every agent operates under a defined supervision policy. Human-in-the-loop checkpoints are not optional add-ons — they are first-class architectural primitives. Supervision is graduated: routine tasks execute autonomously, while high-stakes actions require explicit human approval or are bounded by pre-authorized decision envelopes.
Isolated agents produce isolated outcomes. SOSA mandates an orchestration layer that coordinates agent execution across temporal, informational, and toolchain dimensions. Agents share structured context, respect dependency graphs, and are scheduled according to business-logic DAGs rather than ad-hoc cron triggers.
Security in SOSA is not a perimeter — it is a property of every layer. Each agent runs in an isolated execution environment with scoped credentials, zero-trust network boundaries, and cryptographically verifiable audit trails. No agent can access resources beyond its declared permission set, and all inter-agent communication passes through authenticated channels.
SOSA agents are not scripts with LLM wrappers. They are goal-directed autonomous entities with persistent context, tool-use capabilities, and adaptive planning. Each agent possesses a defined role ontology, success metrics, and failure recovery strategies — enabling them to operate as reliable participants in a larger organizational system.
The current landscape of AI agent deployment can be characterized by two failure modes. The first is under-autonomy: agents configured as glorified chatbots, requiring human input at every step, producing marginal efficiency gains and high interaction costs. The second is over-autonomy: “set and forget” agents with insufficient guardrails that produce cascading errors, hallucinate business-critical actions, or silently drift from their intended objectives.
Both failure modes stem from the same root cause: the absence of a structured methodology for calibrating the supervision-autonomy spectrum. SOSA addresses this by introducing formal governance primitives at the framework level, ensuring that the degree of autonomy granted to any agent is proportional to its demonstrated reliability, the reversibility of its actions, and the risk tolerance of the domain.
In a SOSA-compliant system, agent execution follows a three-phase loop: Plan, Act, and Verify. During the planning phase, the agent decomposes its objective into a sequence of tool calls and information retrievals, subject to the constraints in its capability set. During the action phase, each step is executed against real external systems — APIs, databases, communication platforms — with every interaction logged to an immutable audit store. During the verification phase, the orchestrator evaluates the outcome against declared success criteria and updates the agent's context for subsequent runs.
This loop is not merely procedural. The verification phase feeds into a continuous improvement mechanism: agents that consistently meet their success criteria earn expanded autonomy boundaries, while agents that exhibit failure patterns are automatically escalated to tighter supervision. SOSA thus implements a formal trust gradient that evolves with observed agent performance.
The SOSA methodology directly addresses the three primary barriers to enterprise AI agent adoption: accountability, reliability, and compliance. By requiring full audit trails and graduated supervision, SOSA satisfies regulatory and internal governance requirements without sacrificing operational velocity. By mandating orchestration and structured inter-agent context sharing, SOSA eliminates the coordination failures that plague multi-agent deployments. And by treating security as a first-class design constraint rather than an afterthought, SOSA enables deployment in environments where data sensitivity precludes the use of conventional SaaS-based AI solutions.
Organizations adopting SOSA can expect to deploy AI agents that are not merely impressive in demos, but durable in production — systems that earn trust through verifiable behavior rather than demanding it through marketing claims.
OpsAgent is the first commercial implementation of the SOSA framework. Every architectural decision — from isolated virtual machine 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 18+ agent types across temporal and data dependencies. Agents share context through structured registries, not ad-hoc message passing.
Each client runs on an isolated virtual machine with scoped OAuth credentials. Zero business data is stored on OpsAgent servers. Every action is logged to an immutable audit trail.
Goal-directed agents with persistent memory, real tool access (calendars, CRMs, accounting systems), and adaptive planning — not scripts, not wrappers, not demos.
Every screenshot below is from a live OpsAgent deployment. This is what your operations look like when AI is running the show.
In a world where data security is the #1 business risk, we built OpsAgent on a single non-negotiable principle: your data never leaves your environment.
Every access request is verified. No implicit trust — ever. Permissions are defined, scoped, and auditable.
We don't store your business data on external servers. OpsAgent processes and acts — it doesn't retain.
Integrations connect only to what's needed. No broad permissions, no hidden access. You control what OpsAgent can see.
Every automated action is logged. Know exactly what ran, when, and why — always.
No per-seat fees. No employee counts. Pick the agents you need — pay only for what runs.
Join our private beta. Results-driven. Cancel anytime.
