Features

Built for Execution, Not Prompts

Cerebral is designed for one thing: getting real work done in real systems. Instead of relying on free-form prompts or brittle automations, Cerebral runs work as structured, deterministic workflows. Every step either completes successfully or escalates safely—there’s no partial success, no hidden side effects, and no undefined behavior. This execution-first model makes behavior predictable, testable, and correct by construction, which is what real operations demand.

Under the hood, every action runs through an atomic, reliable runtime. Each step is idempotent, committed once, and protected by retries, backoff, and explicit failure handling. When something fails, it fails cleanly and visibly. When it succeeds, it succeeds exactly once. This allows Cerebral to operate safely across real-world APIs, unreliable networks, and complex dependencies without corrupting state or creating silent errors.

Safe by Design, Confident in Production

Cerebral includes a full dry run mode that executes workflows end-to-end using real data—without writing to production systems. Teams can preview every step, inspect intended actions and branches, and approve changes before anything is allowed to go live. This makes validation fast, safe, and grounded in reality, not simulations. The result is confident rollout, not hopeful deployment.

Not all work finishes in seconds. Cerebral supports stateful, long-running workflows that can pause, wait on time or events, and resume days or weeks later with full state preserved. This makes it possible to automate real operational processes like follow-ups, multi-stage approvals, and cross-system coordination without fragile cron jobs or glue code. Execution stays reliable, traceable, and structurally protected from infinite loops or lost context.

Composable by Nature

Workflows in Cerebral aren’t monoliths. They can call other workflows, forming reusable subroutines and cleanly separated logic blocks. State and context flow automatically between components, which means complex systems can be assembled from simple, testable pieces and evolved over time without rewrites. This modular approach keeps systems understandable, maintainable, and resilient as they grow.

Turning SOPs into production logic doesn’t require custom engineering. Workflows can be authored in a step-by-step editor, defined in JSON, or co-built in working sessions with your team. An LLM-driven workflow creator is coming to accelerate initial setup while keeping the final result structured, auditable, and deterministic.

Full Visibility into Every Decision

Cerebral is observable by default. You can see exactly what’s running, what happened, and why. The Workflow Explorer shows every workflow, step, and branch, with real-time logs, failure paths, and full reasoning traces. Debugging, auditing, and optimization become part of normal operations—not a forensic exercise after something breaks.

Performance is treated as a first-class concern. Cerebral is built for real workloads, not prototypes. The platform supports queue-based execution, rate limiting, backpressure, and per-step timeouts to remain stable under load. The goal is boring reliability: consistent behavior, predictable performance, and graceful failure when the unexpected happens.

Governance, Security, and Control

Execution in Cerebral is governed by policy, not hope. Teams can define approval thresholds, rule-based gating, and escalation paths per workflow, per action, or per customer. This makes it possible to start supervised and move to autonomy when ready—without changing the underlying logic or giving up control.

Security and compliance are part of how the system works, not features bolted on later. Data is encrypted in transit and at rest, sensitive fields are masked before logging, and the architecture supports SOC2-aligned, HIPAA-ready, and GDPR-compliant workflows.

Built for Real Operations

Cerebral is already running complex, multi-system workflows in production. It’s designed to integrate across real tools, enforce real policies, and take responsibility for real outcomes. The goal isn’t to make AI more impressive—it’s to make execution more dependable.

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