Three families of managed agent infrastructure are emerging. All three are often the right answer — for a different trust boundary.
All capability claims on this page reflect public GA and preview documentation as of May 2026. Competitor capabilities change frequently — AWS AgentCore, Microsoft Foundry, Google Agent Platform, Anthropic Claude Managed Agents, and OpenAI Frontier have all shipped material capabilities in 2025–2026. We update this page when material changes occur.
Hyperscaler runtimes (AWS AgentCore, Microsoft Foundry, Google Gemini Enterprise Agent Platform) are capable and increasingly framework-neutral. They are the right choice when cloud residency is acceptable and the customer does not need to independently audit the enforcement layer.
Model-vendor platforms (OpenAI Frontier, Anthropic Claude Managed Agents) are strong within their model ecosystem. When the model vendor and the runtime vendor are the same company, the governance layer and the compliance evidence live in that vendor's infrastructure. That is a trust boundary problem, not a feature gap.
Framework-native platforms (LangChain/LangSmith and others) are strong within a specific development model. Single-framework, cloud-resident. More will emerge as agents proliferate.
All three are often the right answer for cloud-resident buyers who accept vendor-managed governance. cogward is built for a different trust boundary — regulated ISVs and enterprises where customer-owned enforcement is a requirement, not a preference. Managed runtimes optimise for vendor-owned platforms. cogward optimises for customer-owned runtime governance.
LLM gateways such as LiteLLM govern a different layer — model access and routing. They are complementary to cogward, not competitive. See the LLM gateways section below →.
Three structural gaps remain durable regardless of how managed runtimes evolve — because closing them requires changing the trust, ownership, and deployment model, not adding features:
Customer-auditable enforcement. Managed runtime isolation ultimately depends on the vendor's implementation that customers typically cannot inspect or test directly. cogward runs the enforcement layer inside the customer environment.
Customer-owned evidence. Managed runtimes produce logs and audit events. cogward produces a tamper-evident evidence ledger verifiable inside the customer environment, without the runtime vendor as the source of truth.
Ship to every customer. Enterprise customers run across AWS, Azure, GCP, and on-premise. A vendor-tied governance layer becomes a deal blocker for ISVs. cogward provides the same governance model everywhere, so the ISV ships the same governed product architecture to every customer.
| Pillar | AWS AgentCore |
Microsoft Foundry |
Gemini Enterprise Agent Platform |
LangChain Managed |
Anthropic Claude Managed Agents |
OpenAI Frontier |
LiteLLM Enterprise |
Databricks Mosaic AI |
ServiceNow AICT |
Google AX (engine, not runtime) |
cogward |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Pillar 1 — Runtime Customer-owned deployment, bypass resistance, encapsulated engine |
No — vendor cloud | No — vendor cloud | No — vendor cloud | No — cloud only | No — Anthropic cloud only | No — OpenAI cloud only | No — LLM gateway only | No — cloud only | N/A — not a runtime | N/A — engine, not a runtime | Yes |
| Pillar 2 — Authority & Accountability Autonomy class, dual attribution, machine identity per agent |
Partial — IAM, no autonomy class, no dual attribution | Partial — strong Entra Agent ID, per-agent managed credentials, agent registry and lifecycle sponsors; no autonomy class; no hash-chained dual attribution | Partial — cryptographic agent identity, auditable action trail, Agent Registry; no autonomy class; no dual attribution in compliance-record sense | No | Partial — per-tool permissions, managed credential vault; no autonomy class; no dual attribution | Partial — scoped agent identities, enterprise IAM, RBAC; no autonomy class; no dual attribution | No | Partial — Unity Catalog identity | No | No | Yes |
| Pillar 3 — Lifecycle Control Kill switch, drain, scope flexibility, resource controls |
Partial | Partial | Partial | Partial | Partial — long-running sessions, multi-agent orchestration in beta; kill switch semantics not customer-controlled | Partial — agent execution environment with memory and tool use; lifecycle authority not documented as customer-controlled | No | Partial | No | Partial — durable execution primitives only | Yes |
| Pillar 4 — Audit-Grade Evidence Hash-chained, privacy-preserving, GDPR-excisable, dual attribution |
No — engineering telemetry | Partial — audit trail of agent version, deployer, and timing; Microsoft-managed; not hash-chained; not independently verifiable by customer | Partial — per-action audit log via Agent Identity; Google-managed; not hash-chained; not independently verifiable by customer | No | Partial — audit log built in; Anthropic-managed; not independently verifiable by customer | Partial — auditable action logs, SOC 2 Type II, ISO 27001; logs are OpenAI-managed | No — request/response logging; not compliance-grade | Partial — lineage in Delta, not compliance audit | Partial — records policy intent, not runtime enforcement | No | Yes |
| Pillar 5 — Agent Estate Intelligence Per-customer, no cross-tenant pooling, grounded in identity + purpose |
No | Partial — fleet-wide health, cost, and performance monitoring; cloud-based; not per-customer behavioural baselines grounded in declared purpose | No | Partial — LangSmith, cloud-based, cross-tenant | No | Partial — evaluation and optimization feedback loops; built-in, cloud-based | No | Partial — MLflow, data-layer only | No | No | Yes |
Ratings reflect GA / preview state as of May 2026. "Yes" means the pillar is satisfied at the regulated-buyer threshold. "Partial" means the capability exists in meaningful form but does not satisfy the full regulated-buyer threshold — typically because enforcement is vendor-managed and cannot be independently verified, or the audit record is not hash-chained and independently verifiable inside the customer's environment. "No" means absent or not applicable.
Note on ServiceNow AI Control Tower: complementary, not competitive. ServiceNow records what policy should be. cogward enforces it at runtime and feeds the CMDB with real execution evidence.
Note on Google AX: a durable execution engine, not a runtime in the five-pillar sense. One of the engines cogward is built on — not a product evaluated as a runtime replacement.
Note on Anthropic Claude Managed Agents: "Partial" on Pillar 4 reflects that an audit log is present but is Anthropic-managed and not independently verifiable inside the customer environment.
Note on OpenAI Frontier: SOC 2 Type II and ISO 27001 certifications are current. "Partial" ratings reflect that enforcement, isolation, and audit records remain OpenAI-managed regardless of customer data controls.
Note on LiteLLM Enterprise: governs model access at the gateway layer, not agent execution — a different layer from cogward. The March 2026 supply chain compromise (PyPI versions 1.82.7 and 1.82.8) is acknowledged. Self-hosted deployment with enterprise license is available.
Capable governed runtimes for cloud-resident deployments. The structural limits are not feature gaps.
AWS AgentCore and Microsoft Foundry have closed significant gaps since their previews. Both now provide framework neutrality, dedicated agent identity, and policy evaluation. They are the right choice when cloud residency is acceptable and vendor-managed infrastructure is not a constraint. They provide Pillars 2 and 3 partially, and no Pillar 1 (Runtime), Pillar 4 (Audit-Grade Evidence), or Pillar 5 (Agent Estate Intelligence) in the regulated-buyer sense.
The three structural limits that remain: Tier B/C deployment (the runtime, memory, and control plane remain vendor-managed regardless of VPC or BYO VNet configuration), compliance-grade audit (audit logs and trails exist but are vendor-managed and not independently verifiable inside the customer's environment — the regulated-buyer requirement is a hash-chained record the customer's auditors can verify without vendor cooperation), and customer-auditable isolation (the enterprise cannot inspect, pen-test, or verify the enforcement layer without vendor cooperation).
LangChain Managed Deep Agents validates the market.
LangChain built a managed SaaS runtime for their Deep Agents harness. It tells you that even the framework creator agrees: the operational runtime layer is too hard for most teams to own. Their answer is a managed cloud runtime for LangChain users. No self-hosted option. No cross-framework support. No regulated-industry story.
The commercial incentive trajectory is clear: managed offerings receive more investment than self-hosted paths. Teams building production systems on top of a framework's managed runtime are taking a dependency on that framework's ability and incentive to maintain parity. cogward does not have that constraint.
When the model vendor becomes the runtime vendor.
Anthropic and OpenAI have both moved from model providers to managed runtime providers in 2026. The commercial logic is clear: managed infrastructure creates stickier relationships than API access. The governance implications are what matter for regulated buyers.
Capable hosted infrastructure for Claude-based agents. Not customer-owned enforcement.
Launched April 2026. A hosted API service for deploying Claude-based agents on Anthropic's cloud. Long-running sessions, per-tool permissions, managed credential vault, audit log, sandboxed execution, multi-agent orchestration in beta, and Microsoft 365 integration. Audit logs are built in. Sandboxing is real.
The structural limits: Claude-only. Single cloud. No self-hosted path. No cross-framework support. The governance layer is Anthropic-managed. The audit log, the isolation guarantees, the enforcement logic — all depend on Anthropic's implementation, which the enterprise cannot inspect, pen-test independently, or verify without Anthropic's cooperation. When the model vendor and the runtime vendor are the same company, the compliance evidence for what the agent did lives in the same infrastructure as the model itself. For regulated buyers who need the enforcement layer and the evidence record inside their own environment, that is a structural limit regardless of how capable the hosted product becomes.
For cloud-resident teams building Claude-based agents who need hosted infrastructure without maintaining their own orchestration layer, Claude Managed Agents is a capable and improving option.
Enterprise-grade AI coworker platform. Governance layer remains OpenAI-managed.
Launched February 2026. An enterprise platform for "AI coworkers" — agents that connect to enterprise data sources, execute workflows, and operate with enterprise governance controls. Four components: a semantic business context layer, an agent execution environment, evaluation and optimization feedback loops, and enterprise security and governance (IAM, RBAC, auditable action logs, SOC 2 Type II, ISO 27001). Multi-vendor agent support — works with OpenAI, Google, Microsoft, Anthropic, and custom agents. Early enterprise customers include Uber, Intuit, State Farm, HP, and Oracle.
This is more capable than a first read suggests. The multi-vendor agent support and the enterprise IAM story are real. The SOC 2 Type II certification is real. The "AI coworkers" framing positions at the workforce management level — agents with identities that can be onboarded, permissioned, and reviewed — which is closer to cogward's framing than most competitors.
The structural limits are the same as all managed cloud runtimes: the governance layer is OpenAI-managed. Of all the products on this page, Frontier most directly targets the same buyer conversation as cogward — the enterprise platform lead who needs a single governed layer for multiple agents across frameworks and business systems. The differentiation is not features. It is who owns the enforcement layer. Frontier's governance is OpenAI-managed. cogward's is customer-owned, independently auditable, and deployable where no managed cloud runtime can go.
A different layer of the stack. Complementary, not competitive.
LiteLLM is an LLM gateway — a proxy that routes requests across 100+ model providers through a unified OpenAI-compatible API, with enterprise features (SSO, RBAC, team budgets, audit logs, HIPAA compliance) behind a paid license. Self-hostable. SOC 2 Type 2 and ISO 27001 certified.
LiteLLM governs model access. It does not govern agent execution. There is no agent identity, no lifecycle authority, no audit-grade evidence chain, no estate intelligence. It is a peer to the MCP gateway layer inside cogward — governing which models can be called — not a competitor at the control plane level.
The March 2026 supply chain attack (compromised PyPI versions 1.82.7 and 1.82.8 exfiltrated credentials from thousands of installations) illustrates the risk of placing a Python-based gateway between production agents and model API keys without an independent enforcement layer beneath it. Infrastructure-layer enforcement, independent of the agent framework and the model gateway library, is what cogward's Runtime pillar provides.
LiteLLM is what teams use before they need cogward. When agents move from model-calling scripts to production workloads requiring identity, lifecycle, audit, and governance, a model gateway alone is no longer sufficient. The two are additive: LiteLLM governing the model access layer, cogward governing the agent execution layer above it.
Strong on lineage. Requires Delta Lake.
Databricks Mosaic AI is genuinely strong on data lineage, evaluation, and Unity Catalog governance — for teams whose data is already in Delta Lake. It operates on-behalf-of-user identity patterns. No Tier B/C deployment. The governance story is excellent for the data layer; it does not extend to the agent runtime layer for teams outside the Databricks data estate.
Complementary, not competitive.
ServiceNow AI Control Tower records what policy should be. cogward enforces it at runtime. The integration direction: cogward's audit log feeds ServiceNow CMDB with real execution evidence rather than self-reported compliance status. For enterprises already running ServiceNow GRC, this is the expected integration pattern.
Google's consolidated agent governance platform. Cloud-resident with strong identity story.
At Google Cloud Next in April 2026, Google rebranded Vertex AI to the Gemini Enterprise Agent Platform. The platform consolidates model selection, agent development, deployment, governance, and optimisation into a single control plane.
The governance story is now substantive. Agent Identity assigns every agent a unique cryptographic ID with an auditable trail of every action. Agent Gateway acts as a central policy enforcement point for all agent tool calls. An Agent Registry provides a centralised catalog. Fleet-wide monitoring and evaluation tools are included.
The structural limits are the same as all managed cloud runtimes. The enforcement layer is Google-managed — the customer cannot inspect it, pen-test it, or verify audit records without Google's cooperation. There is no self-hosted path and no Tier B/C deployment option. For regulated buyers who need the enforcement layer inside their own environment with independently verifiable compliance evidence, those are structural limits that cannot be addressed by adding features.
An engine cogward is built on.Not a runtime to evaluate in its place.
AX is a durable execution engine. It is not a runtime in the five-pillar sense — it provides none of Pillars 1–5 as customer-facing capabilities. It is one of the engines cogward is built on. The relationship is the same as a managed database to its storage engine: customers procure and evaluate the runtime; backend engine choice is an internal engineering matter.
The customer-facing product is the same regardless of deployment context: deploy cogward, get a governed runtime with all five pillars.