Run anywhere means Catalyst can deploy and manage agent workloads across managed cloud, hybrid, and air-gapped environments without code changes. It abstracts infrastructure via Dapr’s sidecar pattern, so your agent frameworks—LangGraph, CrewAI, or OpenAI Agents SDK—execute consistently. The platform handles durable execution, identity, and tool authorization regardless of the underlying network or compliance boundary.
Catalyst persists workflow state and replays deterministic steps using Dapr Workflows, ensuring agents resume after crashes or network partitions. In hybrid setups, the sidecar communicates with a central control plane over encrypted channels, so state remains consistent across on-prem and cloud nodes. Non-deterministic agent logic—like LLM calls—is treated as side effects; you must design them idempotent for safe recovery.
Yes, Catalyst abstracts infrastructure so you can move agent workflows across environments by changing deployment configuration only. The same Dapr components—state store, pub/sub, secrets—work in air-gapped setups with local replacements. You must ensure any external dependencies (LLM endpoints, MCP tools) are mirrored or proxied inside the air gap. No code changes to your agent framework logic are required.
Catalyst enforces workload identity via SPIFFE-compatible certificates and MCP tool authorization policies, which work offline if PKI is locally managed. All inter-service communication uses mutual TLS, and secrets are stored in a local vault. You must configure network egress rules to block unintended outbound calls. The platform itself does not require internet access once deployed, reducing attack surface.
In air-gapped environments, you must self-host the Dapr control plane components (placement, sentry) and provide local replacements for state stores, pub/sub brokers, and secrets. Catalyst’s management plane is not accessible, so updates and monitoring require on-prem tooling. The agent runtime behavior is identical; only infrastructure provisioning and observability pipelines differ. Plan for offline artifact management for Dapr binaries and sidecar images.
Catalyst provides durable execution logs via Dapr’s workflow status API, showing step-level replay and failure points. For network partitions, check sidecar connectivity to the control plane and state store. Agent-side effects (LLM calls, tool invocations) may timeout; ensure idempotency and retry policies are configured. Use the Catalyst observability dashboard to correlate workflow state with sidecar health metrics.
Yes, MCP tool policies are defined in a portable YAML format that Catalyst applies uniformly across environments. The same policy file can be used in cloud and air-gapped setups, as long as the referenced tool endpoints are reachable locally. No policy logic changes are needed; only the endpoint URLs or service discovery mechanisms differ. This ensures consistent authorization governance.
In hybrid environments, Catalyst leverages a centralized control plane for state management and policy distribution, simplifying operations. Air-gapped setups require a fully distributed control plane with local replicas, increasing infrastructure overhead but offering complete data sovereignty. Both architectures support the same agent frameworks and durable execution guarantees; choose based on compliance needs and operational capacity.
Observability for agent workflows in Catalyst means you get distributed traces, run history, and debugging tools for every agent execution. Traces capture the full path from agent start through each tool call and workflow step. Run history logs every state transition and retry. This lets you replay failed runs locally or in production to diagnose issues without losing context.
Catalyst automatically generates distributed traces for each agent workflow run, linking every step from agent invocation to tool execution and state transitions. Traces follow the OpenTelemetry standard, so you can export them to any compatible backend like Jaeger or Datadog. Each trace includes span metadata for framework-specific actions, enabling precise root-cause analysis of failures or latency.
Yes, Catalyst maintains a complete run history for every agent workflow, including failed ones. You can inspect each run's timeline, state changes, and error messages through the Catalyst dashboard or API. Run history preserves the full execution context, such as input parameters and intermediate state, so you can replay or debug failures without re-running the entire workflow.
Catalyst provides step-by-step debugging for failed agent workflows, including state snapshots and replay. You can pause at failure points, inspect variable values, and resume from the last successful step. Debugging integrates with your agent framework's local tooling, so you can step through LangGraph or CrewAI logic directly. This reduces time spent reproducing issues in production.
Catalyst's observability layer works identically in cloud and air-gapped deployments, using local telemetry exporters and a built-in run history store. Traces and logs are collected within the environment and can be forwarded to your internal monitoring stack via OpenTelemetry. No external connectivity is required for run history or debugging, ensuring compliance with data residency requirements.
Catalyst encrypts all observability data at rest and in transit, with role-based access control (RBAC) for run history and traces. You can mask sensitive data in logs and traces using configurable filters. For air-gapped deployments, telemetry stays within your network. Audit logs track who accessed observability data, supporting compliance with SOC 2 or similar standards.
You can export Catalyst's OpenTelemetry traces and metrics to your preferred monitoring tool, such as Grafana or Datadog, and build custom dashboards there. Catalyst also provides a built-in dashboard with pre-built views for run history, error rates, and latency. For advanced queries, use the Catalyst API to pull raw run data and create visualizations in external BI tools.
Yes, Catalyst attaches unique trace IDs to each agent workflow run, which you can inject into application logs. By configuring your logging framework to include these trace IDs, you can correlate log entries with specific workflow steps. This enables end-to-end troubleshooting across agent runs, tool calls, and your existing application infrastructure.
If a workflow fails silently, check the run history for unhandled exceptions or timeouts. Catalyst logs every state transition, so you can identify where the workflow stopped. Enable detailed tracing to capture tool call failures or framework errors. If the issue persists, use the replay feature to reproduce the failure locally and inspect intermediate state for missing inputs or policy violations.
Catalyst scales agent workflows by distributing Dapr Workflow instances across regions, with state persisted in a backing store. Each agent runner (LangGraph, CrewAI, etc.) runs as a stateless worker that pulls tasks from a durable queue. Workflow state is replicated for regional failover, but you configure the consistency model. Scaling is horizontal: add workers per region, and Catalyst rebalances partitions automatically.
Catalyst supports tenant isolation via separate Dapr application IDs and workload identity per tenant. Each tenant’s agent workflows run in their own namespace, with resource quotas and access policies enforced by Catalyst’s control plane. You map tenants to Catalyst projects, and billing is metered per tenant. Multi-tenancy does not share state or execution contexts across tenants.
Cost control in Catalyst centers on managing workflow execution duration and resource consumption. You set per-workflow timeouts and activity retry policies to cap runtime. Use Catalyst’s monitoring to track state store size and worker utilization. Idle agent runners can be scaled to zero when no workflows are pending. Catalyst bills on active workflow instances and storage, so minimize long-lived workflows and prune completed ones.
Temporal provides durable execution for deterministic application logic with its own SDKs. Catalyst extends this to agentic workloads by supporting probabilistic agent frameworks like LangGraph and CrewAI as first-class runners, without forcing you to rewrite orchestration. Temporal requires custom activities for tool calls; Catalyst integrates MCP for tool authorization and policy. Catalyst also runs in air-gapped environments, while Temporal typically needs cloud connectivity.
Yes, migration is feasible but requires adapting workflow definitions. Temporal workflows use its SDK; Catalyst uses Dapr Workflows with a similar state-machine model. You port deterministic steps directly, while agent runners (LangGraph, etc.) remain unchanged. Catalyst’s MCP integration replaces Temporal’s activity-based tool calls. Plan for idempotency handling on external side effects, as both systems rely on replay but do not guarantee exactly-once for APIs.
Catalyst assigns a workload identity (e.g., SPIFFE-compatible) to each agent runner, authenticating to state stores and tools. MCP tool authorization enforces policies: which agent identity can call which tool, under what conditions. Policies are defined in Catalyst’s control plane and evaluated at runtime. This prevents unauthorized tool access even if an agent is compromised. Workflow state is encrypted at rest and in transit.
Catalyst exposes workflow execution history with step-by-step replay, including agent runner logs and MCP tool call outcomes. Use the Catalyst dashboard to inspect failed activities, retry counts, and state store snapshots. For agent-specific issues, enable debug logging in your runner framework (e.g., LangGraph’s LangSmith). Common failure modes: tool call timeouts, non-idempotent side effects, and state corruption from concurrent workflow instances.
