AI is creating a new
kind of worker. Most organizations don't yet know what that means.

Managing individual MCP connections to every external platform — Salesforce, Snowflake, HubSpot, Slack, your internal APIs — is an operational burden that compounds with every new agent. The Connection Gateway eliminates it. Configure your platform connections once. Mashbot stores and manages credentials, handles OAuth token refresh and rotation, and proxies all tool calls through a single endpoint. Every agent uses one MCP connection. Every call is authorized, logged, and attributable.

Every interaction is logged with full context: who asked, which agent responded, what version was running, what tools it called, what documents it retrieved, and — critically — what the human did with the result. Accepted without change. Edited. Rejected. This outcome signal is the ground truth for continuous improvement. It tells you which skills to refine, which tool calls underperform, and which updates actually made things better. No external observability tool captures this because they never had the outcome data.

Role-based control defines exactly who can change what. Business owners update skills. Operations teams update playbooks. Engineering maintains personas and model configuration. Compliance reviewers manage the policy corpus and clear flagged items. No single stakeholder can change agent behavior alone — the two-key principle ensures business expertise and engineering accountability are both present in every significant change. Every action produces an immutable audit record.

Governance controls who can change an agent. Eval tests whether it performs well. Neither answers the most important question: is this agent being instructed to do the right thing? The Ethics & Policy Compliance layer operates at three levels — instruction integrity on every save, semantic policy alignment review before deployment, and continuous behavioral monitoring after. It catches what access control allows through and what eval gates never anticipated.

Every agent version passes through a structured promotion pipeline before reaching production. Hard gates block any version that fails on factual grounding, scope adherence, PII handling, escalation behavior, or policy compliance — no override path. Soft gates flag quality issues for documented human review. Canary deployment runs the new version alongside the current active version with automatic rollback if quality degrades. The entire record is attached to the version in the registry.

The Governance UI surfaces the entire platform to every stakeholder at the right level of detail — from Platform Admin to general employee. Business owners refine skills and review performance. Compliance reviewers triage flagged items. Engineers manage versions and eval results. And general employees, the people interacting with agents every day, can report incidents and submit feedback directly. One navigation structure, role-scoped access, the right information for every person without the wrong information for any of them.

The Workflow Engine turns isolated agents into governed multi-agent orchestrations. A visual drag-and-drop builder lets operations teams design workflows with branching, looping, parallel execution, and human approval gates — no code required. Each workflow is versioned with the same promotion pipeline as agent versions. Pre-built templates for common patterns — lead qualification, vendor onboarding, incident triage — can be cloned and customized. Temporal powers the execution runtime, so workflows survive restarts, handle retries, and manage human wait states natively.

Regulated industries require employees to hold certifications — HIPAA, PII handling, fair dealing, anti-discrimination. AI agents operating in the same domains should be held to the same standard. Certification Programs define requirements, execute them against agent versions, track validity periods, and trigger automatic recertification when policies change or certifications expire. Required certifications block agent promotion — an uncertified agent cannot reach production. The entire history is auditable: what was tested, what the agent demonstrated, and when certification was granted or revoked.

Every time someone must approve, reject, or sign off on something in the platform — a workflow step, an agent version promotion, a certification sign-off, a flagged skill clearance — it flows through one shared engine. Define approval policies with multi-level chains, role-based routing, required approver counts, and timeout escalation. Approvers get a single unified queue across every approval type, not separate inboxes per feature. Every request, decision, timeout, and escalation is recorded with who, what, when, and why.

An agent can pass every pre-deployment check and still produce problematic behavior in production. Compliance Monitoring watches what agents actually do — detecting unauthorized call attempts, transaction anomalies, behavioral patterns, sentiment drift, and policy boundary testing in real time. Critical violations trigger automatic agent suspension. Every alert follows a structured lifecycle: detection, assignment, investigation, and resolution — producing the documentary record that proves the organization responds to compliance events, not just detects them.