WorkingAgents is the Execution Control Layer for AI agents. Enforcement at the point of action — security, access control, and full audit trails for every agent decision.
New to AI agents? Read our jargon-free executive guide →AI agents can read your databases, call your APIs, send emails on your behalf, and make decisions that affect real customers. Without enforcement at the point of action, every agent is an insider threat.
Every agent-to-tool connection requires its own API keys, OAuth tokens, and service accounts. Credentials scatter across environments with no central control.
An agent deletes a production record, sends a customer email, or leaks PII in a response. Without audit trails, nobody knows until the damage is done.
Most agent frameworks give every agent access to every tool. There's no concept of least privilege. A sales assistant can reach engineering databases.
These gateways are components of the Execution Control Layer. WorkingAgents enforces control at the point of action between your agents and everything they touch.
Unified proxy to 250+ LLMs. One API, automatic failover, smart routing by cost or latency, minimal overhead. Your agents never talk to model providers directly.
Control plane for agentic workflows. Multi-step execution with retries, timeouts, and fallbacks. Works with any agent framework via HTTPS and Secure WebSocket (WSS) APIs.
Enterprise hub for Model Context Protocol. MCP enables connection. It does not enforce control. Centralized tool registry, per-user token management, permission boundaries, and enforcement at the point of action on every tool call.
WorkingAgents deploys inside your VPC, your data center, or your air-gapped network. The Execution Control Layer orchestrates workloads without extracting data. No third-party ever touches your information.
Virtual MCP Servers enforce permission boundaries at the point of action per team, per role, per use case. A sales agent sees CRM tools. An engineering agent sees deployment tools. Neither sees the other's data.
Enforcement at the point of action ensures every agent action, tool call, model request, and guardrail evaluation is captured. When something goes wrong — and it will — you know exactly what happened, who triggered it, and why.
Enforcement at the point of action before, during, and after every tool call your agents make.
Enforcement at the point of action blocks SQL injection, path traversal, prompt injection, and malformed requests before they reach your systems.
Enforcement at the point of action prevents unauthorized execution and requires human approval for high-risk operations. "The agent wants to delete a production table — approve or deny?"
Enforcement at the point of action blocks sensitive data from leaving your perimeter. PII is redacted, credentials are masked, and confidential data is filtered before outputs reach the agent.
| Guardrail | What It Catches |
|---|---|
| Prompt injection prevention | Blocks "ignore all previous instructions" and similar attacks |
| PII detection & redaction | 20+ categories: SSNs, credit cards, emails, phones, addresses |
| Content safety | Hate speech, self-harm, violence with configurable thresholds |
| Topic filtering | Block specific domains: medical advice, legal counsel, financial tips |
| Custom rules | Your own policies, your own logic, enforced at the gateway |
Every previous wave of technology created a new category of risk. Cloud computing created cloud security. Mobile apps created app security. APIs created API security. Each time, organizations learned the hard way that the same capabilities that make technology powerful also make it dangerous when ungoverned.
AI agents are the next wave, and the risks are fundamentally different. A misconfigured API endpoint leaks data when someone finds it. An AI agent without enforcement at the point of action actively seeks out data, makes decisions about it, and takes actions based on those decisions — continuously, at scale, without human review.
A tool waits for instructions. An agent makes decisions. When your AI can decide to query a database, draft an email, and send it — all in a single chain of reasoning — the governance model that worked for tools doesn't work for agents. You need enforcement at the point of action at every step of the chain, not just at the entry point.
Credential sprawl. Five agents connecting to ten tools means fifty sets of credentials scattered across config files, environment variables, and secret managers. No central inventory. No rotation policy. One leaked key exposes everything that agent could access.
Shadow actions. An agent deletes a record it shouldn't have. An agent sends an email with confidential pricing. An agent surfaces PII in a chat response. Without audit trails, these events are invisible until a customer complains or a regulator asks questions.
Privilege escalation. An agent designed for customer support discovers it can also access the billing database, the HR system, and the deployment pipeline — because nobody scoped its permissions. It's not malicious. It's just using every tool available to answer the question it was asked.
Cost explosions. An agent stuck in a retry loop burns through thousands of dollars in API calls overnight. Without token-level monitoring and budget enforcement, you find out when the invoice arrives.
Least privilege. Every agent gets exactly the tools it needs and nothing more. Virtual MCP Servers define permission boundaries per team and use case. The sales agent can't reach engineering tools. The support agent can't modify billing records.
Complete visibility. Every tool call, every model request, every guardrail evaluation is logged with the user, the agent, the inputs, the outputs, and the cost. When the CEO asks "what is our AI doing?" — you have the answer.
Automated safety. Enforcement at the point of action redacts PII before it enters agent context. Prompt injection is blocked before it reaches the model. High-risk actions require human approval. Guardrails enforced at the point of action work regardless of which framework or model the agent uses.
Cost control. Token-level attribution shows exactly which team, user, and use case is consuming what. Budget caps prevent runaway spending. Smart routing sends simple queries to cheaper models and complex queries to capable ones.
WorkingAgents is built by James Aspinwall — a software engineer who got tired of watching AI agents run unsupervised. Every feature exists because a real production system needed it.
We work directly with your team: integration, customization, training, and ongoing support. No ticket queues. No layers of account managers. You talk to the people who build it.
The Execution Control Layer for AI agents — enforcement at the point of action, deployed in your infrastructure.