Key takeaways
- Evaluate runtime behavior directly. Test state recovery, concurrency, isolation and failed tool calls separately; a managed runtime does not make every workflow durable.
- MCP connectivity and authorization are different controls. A tool connection must preserve the intended caller permissions, credential scope and approval rules.
- Separate commercial terms, hosting location and operations. SaaS is not inherently cloud-only, and on-premises is not inherently air-gapped. Check the exact offering and all external dependencies.
- Budget for the complete agent workflow. Include platform fees, compute, model usage, retrieval, tools and maintenance, using current quotations and measured workload inputs.
Choosing an enterprise agent platform means agreeing who runs the agent, how it accesses tools and data, and what the complete workflow costs. Compare the proposed configuration with a representative task, then use the results to define deployment and operating responsibilities.
Production agent runtime evaluation
Useful starting tests include interrupted execution, traffic spikes, and cross-user isolation. Expand the evaluation to reflect the workflow’s failure modes and service-level requirements. The platform team should record how state recovers, how requests are scheduled and where workloads are separated. Test checkpoint recovery, persistent context and code-execution isolation independently.
A representative runtime request flow:
- Client application submits an authenticated request.
- API gateway checks access and request limits.
- Runtime orchestrator schedules the agent.
- Tool execution runs within the configured permission and execution boundaries.
- A state store retains the information needed for continuation or investigation.
Data table · Scroll horizontally to see all columns. Arrow keys work when focused.
| Evaluation area | Candidate or documented example | Test before selecting the runtime |
|---|---|---|
| Managed agent service | Azure AI Foundry Agent Service | Confirm the selected agent type, regional availability, execution isolation and supported recovery behavior. |
| Graph execution | LangGraph documents durable execution and persistence | Interrupt a workflow after a side effect; check checkpoint recovery and duplicate-action handling. Framework behavior and hosted platform features should be evaluated separately. |
| Cloud execution environment | Google Cloud Vertex AI Agent Engine and Amazon Bedrock AgentCore | Confirm runtime, session and tool-service boundaries for the proposed deployment, including quotas and identity propagation. |
| Customer infrastructure | Northflank and TrueFoundry | Request the proposed architecture and test workload isolation, networking, state storage and operator responsibilities. |
| Integrated retrieval and agents | Seahorse Cloud documents managed agents, inference APIs and usage tracking alongside storage and a vector database | Test the intended document-to-agent workflow and confirm the boundary between database tenancy, agent identity and tool execution. |
LangGraph's documented durable execution is useful for workflows that must continue after interruption. Correct recovery still depends on checkpoint configuration and how the application handles external side effects. Test the actual workflow instead of equating the presence of a state store with exactly-once tool execution.
Seahorse's homepage also describes cross-session context memory. Persistent context should be evaluated separately from process recovery: ask what is saved, when it becomes available, which identities can read it and how deletion works.
For every candidate, inject a tool timeout, expired credential and interrupted connection. Check retry limits, backoff, cancellation, duplicate writes and the operator's ability to resume or stop the run. Define the application's responsibilities even when a runtime provides retry mechanisms.
Tool integration, MCP adoption, and credential governance
Connecting agents to enterprise data requires authentication boundaries, standard interfaces and access controls. MCP standardizes interactions between clients and servers; using it does not by itself establish end-user authorization or safe tool behavior. MCP architecture
Credential and tool routing to validate:
- The application identifies the caller and intended action.
- The agent selects an allowed tool.
- The tool server validates the credential and action scope.
- The enterprise system enforces authorization on the actual resource.
- The application records the outcome without exposing secrets.
Evaluate dedicated agent identities and delegated user identities separately. A dedicated identity can reduce reliance on shared credentials, but its assigned privileges may still be excessive. Delegation must be checked end to end: an authenticated user should not silently become a broadly privileged service account at a downstream tool. Expired tokens, revoked access and cross-tenant requests belong in the test set.
Cloud-agent and gateway candidates such as Azure AI Foundry, AgentCore and TrueFoundry should demonstrate the identity flow for the proposed tools. Ask which component issues and validates tokens, where secrets reside, how scopes are restricted and whether a human approval step can gate a consequential action. Record the component responsible for each control.
Seahorse Cloud documents MCP tool calls for managed agents, together with API-key authentication and tenant isolation in its database. IT should demonstrate how these features connect to the application's identity model. Test a permitted read, a denied read and a denied action at the downstream tool.
Network controls provide another layer: inventory required destinations, restrict unnecessary egress and inspect what tool inputs, model calls and telemetry leave the environment. Network isolation complements authorization; neither replaces the other.
Operating models and deployment boundaries
Deployment decisions should be recorded along independent dimensions, rather than on a fixed SaaS-to-BYOC-to-air-gap spectrum.
Data table · Scroll horizontally to see all columns. Arrow keys work when focused.
| Dimension | What to establish for the proposed offering |
|---|---|
| Commercial model | Subscription, software license, service agreement and applicable edition |
| Hosting location | Vendor environment, customer cloud account or on-premises infrastructure |
| Operator responsibility | Who provisions, patches, scales, backs up and restores each component |
| Management plane | Where configuration, identity metadata, logs and support access reside |
| Network dependencies | Model inference, embeddings, parsing, telemetry, licensing, updates and external tools |
SaaS describes a service delivery model and can coexist with an on-premises arrangement when the supplier offers it. BYOC identifies an infrastructure boundary, but does not by itself determine who operates the system. On-premises support does not prove disconnected operation.
LangChain's self-hosted deployment documentation is an example of why edition and architecture matter: evaluate the proposed self-hosted components and license separately from a hosted service. For cloud-managed candidates, confirm the regions, private connectivity and service-specific data flows in the actual proposal. For Northflank or TrueFoundry deployments, request the same component-by-component inventory instead of assuming all management data stays in the workload cluster.
Seahorse Cloud documents Kubernetes-native architecture and on-premises or SaaS delivery. It combines S3-compatible storage, a vector database, document parsing and managed agents. Confirm the offered combination and the operator for the underlying cluster; Kubernetes-native does not assign cluster maintenance automatically to the customer.
If disconnected operation is required, test the complete installation with external connectivity restricted. Include model and embedding endpoints, parser dependencies, package retrieval, license validation, updates and support procedures. Obtain an explicit support statement for that configuration before treating it as an air-gapped offering.
Observability and step-level cost attribution
Because an agent can invoke several tools, retrieval steps and model requests during one interaction, a useful trace follows the complete run. Measure cost at the granularity that the selected services actually expose; token counts alone do not capture runtime compute or external tool charges. Anthropic's research on multi-agent systems (published June 13, 2025) noted that in its internal workloads, single-agent workflows consumed approximately 4 times more tokens and multi-agent workflows approximately 15 times more tokens than chat interactions. These figures reflect token volume within specific benchmark tasks rather than industry-wide averages or direct total-cost multipliers.
Example trace fields tailored to application and data-handling requirements:
- Request: caller, project and correlation identifier; must redact credentials and sensitive arguments.
- Model step: model/version, input and output usage, duration and termination reason.
- Tool step: tool name, permitted action, duration and result or error; redact credentials and sensitive arguments.
- Retrieval step: query configuration, source identifiers and latency, subject to the organization's data-retention policy.
- Completion: final outcome and attribution to the intended team or application.
Ask each provider to demonstrate a failed run, its intermediate steps and the usage record needed to reconcile the invoice. Check which events are available through the product itself, which require an SDK or application instrumentation, and which can be exported. If audit retention or immutability is required, ask the security owner to test those controls separately.
Seahorse's product page documents vector database monitoring and managed-agent usage tracking. Ask the supplier for a sample usage export. The buyer can then check whether its fields and granularity support per-run cost reconciliation, and confirm the retention period.
Keep AgentOps distinct from RAGOps. RAGOps covers pipeline composition, operation and monitoring; AgentOps covers agent creation, deployment, management and monitoring. MCP supplies data and tool integration to those workflows. An evaluation should exercise an agent revision, a failed tool call, a deployment change and a source update, with a named owner for each recovery path.
Pricing structure comparison and TCO modeling
Total cost of ownership (TCO) for agent platforms comprises model fees, runtime compute, platform charges, data services and internal engineering work. Estimate the same workload and period for every proposal.
Estimated operating cost for the chosen period =
platform and support fees
+ runtime compute and persistent state
+ model inference and embeddings
+ retrieval, tool, storage and data-transfer charges
+ internal operations and maintenance labor
Data table · Scroll horizontally to see all columns. Arrow keys work when focused.
| Candidate / Platform | Billing structure and metering units | Key cost considerations |
|---|---|---|
| LangGraph OSS | Free open-source framework | Compute infrastructure, storage, and underlying model inference costs are billed separately. |
| Amazon Bedrock AgentCore | Serverless microVM runtime metering | Actual CPU consumption and peak memory consumed up to each second are metered separately. CPU-inactive I/O wait incurs no CPU charge; memory remains billable during the session. Optional feature modules act as separately metered services. |
| Azure AI Foundry Agent Service | Varies by selected services and agent type | Billing depends on specific underlying Azure service components; confirm current rates via official documentation or quote. |
| LangSmith | Commercial managed platform / enterprise deployment | Hosted platform and self-hosted commercial features are billed separately from the open-source framework; verify current tiers and unit rates with official pricing schedules. |
| Seahorse Cloud | SaaS or on-premises deployment | Commercial terms are available upon request (quotation required). |
Data table · Scroll horizontally to see all columns. Arrow keys work when focused.
| Cost category | Inputs to obtain | Sensitivity to test |
|---|---|---|
| Platform and support | Current quote, edition, included usage and support scope | Additional environments, seats or service tiers |
| Runtime | Billable compute, idle-time policy, concurrency and minimum capacity | Bursty and sustained traffic |
| Models and tools | Actual token usage and external API charges | Longer contexts, retries and multi-step loops |
| Retrieval and state | Stored volume, queries, writes and update frequency | Corpus growth and reprocessing |
| Operations | Integration, patching, evaluation and incident hours | Connector changes and more demanding recovery targets |
Use current vendor pricing documentation or an official quote for each line item. Readers should verify active terms directly with suppliers prior to commitment. Do not assume a free tier applies to an enterprise configuration or that a managed service has no minimum commitment. Keep platform charges distinct from underlying cloud and model-provider bills.
For Seahorse, request the current commercial terms for the selected deployment in the vendor's official quoted currency. Include the quotation date, plan, region and metering conditions so the comparison can be checked when usage or terms change.
Set alerts and application limits together. An alert can notify an owner after usage occurs; it does not necessarily stop spend. Test request limits, maximum agent steps, tool-call caps and cancellation behavior, then reconcile a representative run against billed usage.
The buyer should compare retained integration, permissions, evaluation and incident duties with the service agreement. Include those hours in the estimate so transferred maintenance work produces a visible cost difference.
FAQ
Can we expose an agent as an API endpoint instead of using a vendor-provided chat UI?
An API-based deployment can serve an internal portal or backend workflow. Seahorse documents inference APIs for managed agents. For any candidate, confirm the selected endpoint's authentication, streaming behavior, request limits and integration contract rather than assuming every platform supports the same REST or gRPC interface.
How do we avoid broadly shared credentials during tool execution?
Use narrowly scoped runtime and tool identities, and test any required end-user delegation. Verify authorization at the downstream resource, rotate credentials and inspect denied requests. Dedicated identities alone do not prevent privilege escalation.
Which deployment can run without public internet exposure?
Select an offering explicitly supported for the required network boundary. Self-hosted or on-premises software may still use external models, parsers, telemetry or licensing services. Seahorse's documented on-premises option is an evaluation starting point, not proof of disconnected feature parity.
What causes unexpected invoice spikes in production agent deployments?
Repeated tool calls, retry loops, longer model contexts, higher concurrency and corpus reprocessing can all increase usage. Trace a representative run, set application limits and verify which provider budget controls alert and which actually enforce a stop.