Platform
Secure and govern autonomous AI agents
ForgeCrux · confidential architecture
Agentic AI reference architecture
End-to-end workflow from product intent to production systems — roles, fabric, gateways, MCP, and backends.
Roles & interfaces
Business intent, architecture, and delivery surfaces.
Product owner
Business logic & outcomes
Architect
Blueprint & controls
Developer
Code, commit, certify
Cursor
IDE / Codex
CLI
Web apps
Agent fabric
Intelligence, traffic, identity, and specialized workers.
ForgeCrux AI Gateway
Routing · RBAC · guardrails · spend · traces
Orchestration engine
Multi-step tasks & decomposition
Agent catalog
Governed inventory of specialists
Agent builder
Low-code + GitOps definitions
Doc agent
Test agent
Code agent
Infra agent
LLM pool
GCP · AWS · Azure · self-hosted
Agent & MCP plane
Secure interoperability from agents to the real world.
ForgeCrux Agent Gateway
Identity · A2A · HITL · Kubernetes runtime
Contextual
interoperability
ForgeCrux MCP Gateway
Discovery · vault · tool RBAC · audit
Secure
integration
API layer
Salesforce · SAP · ServiceNow · custom APIs
Backends & infrastructure
Systems of record, delivery, and clouds.
Databases
Vector · SQL · NoSQL
Legacy systems
SOAP · mainframe · ESB
CI / CD
GitOps · tests · evals
Clouds
AWS · Azure · GCP · private
Control path: Policy · Identity · Observability across every hop
Data path: Channel → Agent Gateway → AI Gateway → MCP → API → SoR
Identity for every agent
No anonymous bots. Each agent is named, credentialed, and scoped like a production workload.
Controlled autonomy
Policies decide which models, tools, and data an agent may use—and when a human must approve.
End-to-end traces
Follow a task from user request through agent steps, model calls, MCP tools, and enterprise systems.
Key Capabilities
Complete Agent Gateway capabilities
Everything required to publish, secure, mediate, observe, and operate agent gateway workloads on ForgeCrux.
Registry, identity, and lifecycle
Treat agents as production services, not scripts.
- Agent registry with owners, versions, and environments
- Unique identity, mTLS, and workload tokens per agent
- Metadata: purpose, risk tier, data classification
- Draft, review, certify, deploy, pause, and retire workflows
- Blue/green and canary releases of agent versions
- Multi-tenant isolation and namespace quotas
- Framework support: LangGraph, CrewAI, Google ADK, OpenAI Agents, custom
- CI/CD hooks to block uncertified agents from production
Authorization, routing, and A2A
Control who an agent is, whom it may call, and how work is dispatched.
- Agent-to-agent (A2A) messaging with authenticated channels
- Allow lists for peer agents, models, tools, and APIs
- Task routing by skill, load, cost, latency, and policy
- Delegation, supervisor, and swarm patterns with hop limits
- On-behalf-of user context with consent and scope reduction
- Session affinity and sticky routing for long-running tasks
- Failover and retry when an agent or tool is unhealthy
- Rate limits and concurrency caps per agent and per tenant
Tools, memory, and data access
Bound what agents can remember and which systems they can touch.
- Tool access only through MCP Gateway and API Gateway
- Least-privilege tool packs per agent role
- Memory stores with TTL, encryption, and tenant isolation
- No cross-agent memory unless explicitly shared
- Retrieval policies and forbidden corpus lists
- File, browser, and code-exec sandboxes with egress control
- Long-running workflow state with resumability
- Redaction of secrets and PII from memory and logs
Guardrails, HITL, and safety
Keep autonomous loops inside enterprise bounds.
- Step, time, token, and cost budgets per run
- Human-in-the-loop approvals for high-risk actions
- Kill switch, pause, and drain for runaway agents
- Prompt, tool, and output guardrails inherited from AI Gateway
- Goal and policy constraints (cannot exfiltrate, cannot spend above N)
- Simulation and red-team suites before production
- Break-glass with dual control
- Incident playbooks and automatic containment
Observability, evaluation, and cost
See every step and prove agents are doing the right work.
- Step-level traces: thoughts, model calls, tools, and results
- Workflow DAG views and replay
- Success, failure, loop, and handoff metrics
- Quality evals: task completion, groundedness, policy adherence
- Online feedback and offline golden-task regression
- Cost by agent, team, model, and tool
- OpenTelemetry-compatible export
- Alerts on loops, spend spikes, and policy violations
Platform and operations
Run agentic systems with the same rigor as APIs.
- Agent APIs, CLI, SDKs, and event webhooks
- Kubernetes, Terraform, and GitOps for agent configs
- Environments and promotion of agent definitions
- Multi-region active-active agent runtimes
- SLA, capacity planning, and fair-share scheduling
- Audit of identity, policy, and deployment changes
- VPC, on-prem, and air-gapped execution
- Unified console with API, AI, and MCP gateways
How teams run Agent Gateway on ForgeCrux
Register agents
Onboard each agent with an owner, identity, risk tier, and environment before it can call models or tools.
Attach permissions
Grant only the model routes, MCP tools, and APIs that agent needs. Default deny everything else.
Set budgets and HITL
Cap tokens, dollars, and tool calls. Require human approval on irreversible or high-value actions.
Trace every run
Turn on step-level tracing so support and security can replay what an agent did.
Evaluate before scale
Run simulations and quality gates in CI; promote only certified agent versions.
Operate in production
Use kill switches, canaries, and spend alerts so autonomous systems stay inside SLO and policy.
Agent Orchestration Flow
Multi-step agent workflows governed at every hop.
User Request
Chat • Workflow • API
Agent Gateway
Identity • Auth • Policy
AI Gateway
Model Selection • Guardrails
MCP Gateway
Tool Discovery • Execution
Enterprise Systems
APIs • Data • SaaS
Agentic AI Orchestration
Enable intelligent multi-step reasoning with full control and visibility.
User / App
Request • Workflow
Agent Gateway
Identity • Policy
AI Gateway
Model • Guardrails
MCP Gateway
Tools • Resources
Response
Action • Audit • Trace
Related Products
MCP Gateway
ForgeCrux MCP Gateway is the governed fabric for Model Context Protocol: server registry, tool discovery, OAuth, RBAC, credentials, virtual servers, traffic control, and full audit of every tool call.
AI Gateway
ForgeCrux AI Gateway is the single endpoint for multi-model access, intelligent routing, prompt control, guardrails, token and cost management, evaluation, and LLM observability—across OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, and self-hosted models.
Observability
ForgeCrux One Observability is the telemetry plane for APIs, LLMs, MCP tools, and agents. Platform, SRE, security, and FinOps teams ingest logs, metrics, and traces from every gateway hop, then act from one dashboard—maps, alerts, incident tools, and audit replay included.
Ready to get started with Agent Gateway?
Talk to our team about deploying Agent Gateway in your enterprise environment.