Workflow orchestration tools for AI agents coordinate LLM-driven, non-deterministic steps (agents, tools, model calls, human approvals), whereas Airflow or Temporal were built for deterministic data workflow orchestration. We typically combine both: Temporal or Dagster for the deterministic backbone, LangGraph or a custom orchestrator for the agentic layer. That gives you reliability where it matters and flexibility where agents need to reason.

No. We wrap existing langchain multi agent prototypes in the platform runtime, adding checkpointing, observability, guardrails and CI/CD around them without rewriting business logic. In most engagements 70-80% of the original agent code is preserved, and only the plumbing (state, retries, tool routing, evals) is replaced with platform components.

We treat non-determinism as a first-class problem. Every multi-agent workflow runs inside a typed state machine with checkpoints, so failed steps replay instead of restarting the whole run. We add eval harnesses on every critical node, deterministic fallbacks for high-risk steps, and circuit breakers that escalate to humans when confidence drops below a threshold.

Yes. The platform is cloud-native and deploys on AWS, Azure, GCP or on-premises Kubernetes. It connects to on-prem systems through private links, service meshes, or self-hosted MCP/tool gateways. No customer data or prompts leave your VPC unless you explicitly enable an external model, and even then traffic flows through the governed model gateway with logging and PII controls.

We enforce cost control at four layers: model routing (use the cheapest model that passes evals), semantic caching, per-workflow and per-team spend caps, and token budgets inside each agent loop. Customers typically see a 30-50% cost reduction in the first quarter after the model gateway and caching are enabled, with no measurable quality loss.

Yes. We include structured LangChain training and LangGraph workshops as part of delivery, typically a 2-day hands-on bootcamp plus pair-programming during the build phase. The goal is that your engineers own the platform after handover and can design, ship and operate new agent workflows without depending on us.

The platform plugs into your existing CI/CD (GitHub Actions, GitLab CI, Azure DevOps, Jenkins) and treats prompts, agents, tools and eval sets as versioned artifacts. Every change runs through automated evaluations before promotion, so you get the same review, testing and rollback discipline for AI workloads as for any other production service.