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Infrastructure
Our agent workflows are durable, fault-tolerant, and built for production. No timeouts. No lost progress. No silent failures. Every workflow checkpoints its state and resumes exactly where it left off.
Workflows run for minutes, hours, or days with full state checkpointing and zero timeouts.
Transient failures retried with exponential backoff. A flaky API call never kills a workflow.
AI output streams token-by-token to your frontend. Users see results as they happen.
Workflows pause for human approval at critical decision points and resume seamlessly.
Durable Execution
Traditional AI integrations break on timeouts, lose state on crashes, and fail silently. Our durable execution model eliminates every one of these failure modes.
Workflows run for minutes, hours, or days without hitting arbitrary time limits. Long-running AI tasks complete on their own schedule.
Transient failures are retried with exponential backoff. A flaky API call or temporary network issue never kills an entire workflow.
Every workflow step is checkpointed. If a server restarts or a step fails, execution resumes from the last successful checkpoint, not from scratch.
Patterns
Five battle-tested patterns for composing AI agents into reliable, production-grade workflows. Each pattern solves a distinct class of orchestration problems.
Sequential steps where the output of one AI call feeds into the next. Each step validates and transforms data before passing it forward. Ideal for multi-stage content generation and data processing pipelines.
Intelligent classification directs tasks to specialized models or handlers. A single entry point routes requests based on intent, complexity, or domain to the most capable agent for the job.
Fan-out work across multiple agents simultaneously, then aggregate results. Run code review, security audit, and performance testing in parallel to cut total processing time by an order of magnitude.
A central orchestrator decomposes complex tasks into subtasks, delegates them to specialized worker agents, and assembles the final output. The pattern behind our autonomous development system.
An evaluator agent scores the output of a generator agent, then feeds structured feedback back for refinement. Iterates until quality thresholds are met. Used in our code review and content quality pipelines.
Streaming
AI agent output streams live to your frontend the moment it is generated. No polling, no waiting, no stale data. Your users see results as they happen.
AI-generated text streams token by token to the frontend. Users see results appearing in real time instead of waiting for a full response.
Workflow state changes propagate as structured events. Your frontend knows exactly when a step starts, completes, errors, or needs attention.
Built-in flow control ensures fast producers never overwhelm slow consumers. Streams adapt to network conditions and client processing speed.
Oversight
Critical decisions should not be fully automated. Our workflows can pause at any step, wait for human approval, and resume execution seamlessly.
The agent identifies a step that requires human judgment, such as approving a deployment, reviewing generated content, or authorizing a financial transaction.
The workflow checkpoints its state and sends a notification via webhook, email, or Slack. No resources are consumed while waiting.
The reviewer sees full context: what the agent did, why it paused, and the proposed next action. Approve, reject, or modify before continuing.
On approval, execution picks up from the exact checkpoint. No re-processing, no data loss, no cold starts. The workflow continues as if it never paused.
Integrations
Connect your AI workflows to the rest of your infrastructure. Trigger them from anywhere, schedule them to run on their own, or process data in bulk.
Trigger workflows from any external event. Incoming webhooks are validated, queued, and processed with guaranteed delivery and idempotent execution.
Cron-based scheduling for recurring workflows. Daily reports, weekly audits, monthly analyses, all running autonomously with full observability.
Process thousands of items through an AI pipeline with automatic parallelism, rate limiting, and progress tracking. Failed items retry independently without blocking the batch.
The Bottom Line
Durable execution with automatic checkpointing
Zero-downtime deployments for running workflows
Full observability with structured logging
End-to-end type safety across the pipeline
Sub-second cold starts for on-demand workflows
Built-in rate limiting and backpressure
Automatic scaling based on queue depth
Encrypted data at rest and in transit
30 minutes. No commitment. Walk us through your use case and we will show you exactly how durable AI workflows fit into your stack.
If automation is not the right answer, we will tell you.