Enterprise Workflow Automation
Any AI Agent Can Develop

Backed by a durable execution framework for building unbreakable, cloud‑native distributed systems in effortless, uninterrupted agentic loops.

Get Started View Examples
Workflows.cs
public async Task<String> CloneToStaging(String orgId) {
    // C#
    String dbUri = await CMDAsync($"ProvisionStagingDb --orgId {orgId}");
    // Pipelines (cross-shell/language parallel streaming)
    String snapshot = CMDV($"ExportDb --uri {dbUri}") | CMDV("Compress");
    // Python — calls Python script over HTTP/2 (sub-ms latency)
    LocalTerminal.Shell = python;
    String report = await CMDAsync($"validate_schema --snapshot {snapshot}");
    // Java
    LocalTerminal.Shell = bash;
    String cert = await CMDAsync($"java -jar certify.jar --report {report}");
    return $"Staging ready: {dbUri}, cert: {cert}";
}

Instant resume. Continuous development.

On workflow failure, code can be updated and restarted. Previously completed steps will recover in parallel and the execution will resume instantly as if nothing happened. This enables building workflows using massive, real-world datasets in an uninterrupted development loop. Equivalent to journal-based replay, but performance bottlenecks are bypassed through a distributed storage architecture.

Durable recovery illustration

AI can build. You can visualize.

No special rules or specialized infrastructure. AI can build any workflow effortlessly and the progress can be verified through auto-generated diagrams that visualize how workflows are defined and how they run.

AI operability illustration

Multi-language workflows. Any language.

Workflows can be written in multiple languages at once — such as Python, Java, JavaScript, and Bash. No glue infrastructure, message brokers, or service boundaries between languages.

PolyglotPipeline.cs
public class Workflows(PythonTool python, Bash bash) {
    public async Task<String> DeployPipeline(String env = "prod") {
        // C#
        String validated = await CMDAsync($"ValidateSchema --artifact data/raw.csv --schema v2 --strict");
        // Python — calls Python script over HTTP/2 (sub-ms latency)
        LocalTerminal.Shell = python;
        String transformed = await CMDAsync($"transform_data --input {validated} --format parquet");
        // Bash
        LocalTerminal.Shell = bash;
        String artifact = await CMDAsync($"tar -czf - ./dist | aws s3 cp - s3://releases/{env}/$(date +%s).tar.gz");
        // Java
        String signed = await CMDAsync($"java -jar signer.jar --input {artifact} --keystore /etc/keys/prod.p12");
        // JavaScript
        await CMDAsync($"node notify.js --event deployed --artifact {artifact} --sig {signed}");
        return $"Deployed: {artifact}";
    }
}

Plain code. No constraints.

Business logic remains plain code — free of special rules, restricted APIs, separate orchestration layers, or new paradigms. Systems requiring strict safety can leverage standard static analysis tools to guarantee higher safety standards than systems relying solely on determinism validation at runtime. Anyone and any AI can use it immediately.

AuditWorkflow.cs
// Workflow — no decorators. No base class. Just a method.
public async Task<String> AuditOrganization(String orgId) {
    logger.LogInformation("Verifying registry for: " + orgId);
    await Task.Delay(300);
    String registryState = await CMDAsync("VerifyCorporateRegistry --orgId " + orgId);
    if (registryState.Equals("entity-active")) {
        await Task.Delay(200);
        return "Audit Pass: " + registryState;
    }
    throw new InvalidOperationException("Compliance failed for: " + orgId);
}
// Workflow step — also just a method.
private async Task<String> VerifyCorporateRegistry(String orgId)
    => await registryClient.GetStatusAsync(orgId);

Scaled automatically. No limits.

A single workflow can span millions of parallel branches and run for months without limitations. No event history caps. No forced workarounds at scale. The platform operates as HTTP server middleware and can leverage any standard traffic management technology.

Distributed execution illustration

Runs anywhere. No separate server.

Cloud-native architecture that unifies everything into a single application compatible with any infrastructure. No dedicated cluster, sidecar, or daemon. No new operational burdens. Can run anywhere — serverless or on-premises, and Calq Relay can deploy it to Kubernetes with ease.

Infrastructure illustration

Open ecosystem. Integrated with GitHub.

Can be used to build standalone applications. Such applications can be transformed into CLI tools via Calq CLI and deployed as GitHub Actions using Calq Flow. On Github, there's no need for Redis or extra setup.

CI/CD durability illustration

Built-in Web dashboard. Real insights.

Workflow definitions can be browsed even before workflows run. Timeline and flow diagrams show real-time logs per step. This way, workflows can be fully understood without looking at the code.

Execution Flow Tree View
Execution Timing Timeline View

Use Cases

AI

High-performance AI systems that execute without repeating expensive model calls.

Business Process Automation

Approval chains, onboarding flows, and multi-step operations with human checkpoints.

Long-running Workflows

Processes that can survive crashes and run for months without hitting limits or requiring workarounds.

Critical Transactions

Multi-step operations that can be trusted to recover from failures and complete successfully.

CI/CD

Complex pipelines without complexity burden.

Large-Scale Batch Processing

Workloads that process millions of parallel jobs and effortlessly resume on mid-execution failures.

Questions or Need Support?

Get in touch with our team for technical support, licensing questions, or partnership opportunities.

[email protected]

or reach Greg Chuchro directly on LinkedIn

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