AgentBeam: One-Command Local Security Layer for AI Agents

If your coding agent can run Bash, edit files, and call MCP tools, your security model is no longer "don't paste secrets into ChatGPT." It is "what did the agent actually try to do in the last hour — and was it allowed to?" explainx.ai has tracked that gap through incident after incident in 2026. AgentBeam is the local security layer the team behind Beam CLI now ships: a single beam setup command wires native hooks into every supported agent on your machine, applies policy locally, and reports approved telemetry to a dashboard — no manual collector start, no editing agent config files by hand.

This is a meaningful shift from the tool's earlier design. Beam CLI's rule was "observation must never become enforcement." AgentBeam's is closer to "enforce locally, report what happened" — depending on organization or user policy, it can ask for approval, block, redact, or log a proposed agent action before it runs, not just flag it afterward for a human to review in Studio.

TL;DR

What AgentBeam is (and how it differs from Beam CLI)

AgentBeam is the local security layer; beam is the AgentBeam CLI. Where the earlier Beam CLI positioned itself strictly as a passive collector — normalizing hook payloads, redacting secrets, and surfacing heuristic findings for a human to review in Studio — AgentBeam's hook and policy decisions run locally and can act, not just record. That means actions can be blocked even when the collector or dashboard is unavailable, which is a stronger guarantee than a cloud-dependent guardrail can offer.

Depending on the organization's policy, AgentBeam can:

• Ask for approval before an agent proceeds with a flagged action
• Block the action outright
• Redact sensitive content out of a tool call or MCP response before it reaches the agent
• Log the event for later review, the old Beam CLI behavior, when policy doesn't call for stronger intervention

That range — from silent logging to a hard block — is a deliberate design difference from tools like Destructive Command Guard, which denies known destructive shell patterns inline with no policy tiering, and from pure observability tools that never intervene. For teams already running MCP and skill verification workflows, AgentBeam adds a continuously enforced policy layer on top of a one-time install scan.

Install and setup: one command, not a git clone

The install path has been simplified from Beam CLI's git clone + npm run setup:global flow to a standard npm global install plus a single setup command:

npm i @agent-beam/beam -g
beam setup

beam setup is the main command, and it does all of the following in one pass:

1. Detects which supported agents are actually installed on the machine
2. Installs each detected agent's native AgentBeam hook, merging non-destructively into existing configuration
3. Inventories configured MCP servers
4. Connects the device to the AgentBeam dashboard
5. Downloads the organization's and user's policy
6. Starts the persistent background service

After setup finishes, selected agents are protected automatically. There is no separate beam run step and no manual collector process to keep alive — this removes the biggest friction point from the earlier npm run setup:global / beam start / beam service install sequence, where forgetting the service-install step meant protection didn't survive a terminal close.

Run beam setup again whenever you install a new agent or need to repair a broken install. It preserves your existing agent configuration rather than overwriting it.

Useful commands

beam setup                 # install or repair AgentBeam and agent protection
beam service status        # check the background service
beam agent list            # see supported agents and hook status
beam studio                # open the local activity dashboard
beam sync                  # fetch the latest organization policy

beam sync matters operationally: when an admin tightens org policy (say, adding a newly-banned MCP server or a new destructive-command pattern), machines don't get the update until they sync — either automatically on the service's schedule or manually via beam sync when you need the change immediately.

What AgentBeam protects

Depending on organization policy, AgentBeam can ask for approval, block, redact, or log:

• Environment variables and .env files
• API keys, tokens, passwords, private keys, and cloud credentials
• File access outside the current workspace
• Destructive commands and production changes
• MCP write or side-effect tool calls
• Attempts to modify AgentBeam policy or data, stop the AgentBeam service, or uninstall AgentBeam

That last category is worth pausing on: AgentBeam explicitly hardens itself against an agent trying to disable or tamper with its own protection, which was not a stated design goal of the earlier observation-only Beam CLI.

MCP responses can also be filtered locally before sensitive content reaches an agent — not just logged after the fact. MCP inventory and activity are reported to the dashboard so administrators can review or block a specific server for one user or for the entire organization.

Multi-agent support

AgentBeam is not Claude-specific. beam setup discovers whichever agents are actually installed on the machine and installs the appropriate native hook for each one it finds and you select.

Agent configuration is updated non-destructively — AgentBeam merges its hook into ~/.claude/settings.json, ~/.cursor/hooks.json, and equivalent files for the other agents rather than replacing whatever hooks were already there. That matches the merge behavior Beam CLI's beam agent install <agent> used, just folded into the single beam setup flow instead of a per-agent command.

The dashboard

After enrollment, the AgentBeam dashboard shows agent activity, policy decisions, MCP inventory, users, installations, model usage, token counts, violations, blocked actions, and sensitive-data findings. Administrators can block MCP usage for an entire organization or for an individual user from the same view.

This is a materially broader surface than Beam Studio's original Activity / Findings / Scan panes, which were scoped to a single machine's local events. The dashboard is where the multi-seat, org-policy story lives — a single laptop's beam studio view still works for local activity, but org-wide visibility and MCP blocking are dashboard features.

Privacy and local operation

AgentBeam's hook and policy decisions run locally, so actions can still be blocked even when the collector or dashboard is unavailable — the enforcement path does not have a hard dependency on connectivity. Telemetry is redacted before storage or forwarding, the local service binds to loopback, and AgentBeam's own files are protected from modification or removal by agent hooks (see the self-tampering protection above).

This local-first design is the same principle Beam CLI shipped with — a loopback-bound collector, redaction before disk — carried forward into a product that now also enforces rather than only observing.

The 20 security skills and seven reviewer roles (unchanged)

The bundled Skills/ library still ships 20 workflows across coordination, pre-install discovery, AI trust boundaries, application/supply chain, and deployment/operations — for example security-assessment, prompt-injection-review, dependency-supply-chain, and agent-incident-response. Seven paired reviewer sub-agent profiles live under Skills/agents/claude/ and Skills/agents/codex/ for Claude Code and Codex specifically.

These are plain markdown workflows, not hidden CLI subcommands, and they don't require beam setup, enrollment, or a dashboard connection — copy the folders you need into your agent's skills directory and run them on real artifacts:

• Review a downloaded skill folder before install, including referenced scripts.
• Review an MCP config and server source before connection.
• Inventory AI assets in a repo, then assess model loaders and retrieval permissions.
• Review exported agent events and separate proposed actions from confirmed effects.

Deeper setup and spec background lives on AgentBeam's skills documentation.

How AgentBeam fits next to other safety layers

Think in layers:

1. Least privilege and sandboxes — OS-level boundaries (Fable-OS-style capability models when commands are not the whole story).
2. Inline command policy — dcg and similar hooks that always deny known destructive shell patterns.
3. Local enforcement and observation — AgentBeam's hooks, local policy engine, dashboard, and skills library, now covering both blocking and visibility instead of visibility alone.
4. Hosted governance — org-wide policy management and multi-seat visibility in the security platform landscape when one laptop's protection isn't enough.

explainx.ai's Sentinel product direction covers broader desktop and browser monitoring; AgentBeam is the piece focused specifically on coding-agent hook interception and local policy enforcement.

Honest limitations (read before you rely on it)

• Enforcement quality still depends on policy — AgentBeam can block, but only for what your organization's or your own policy actually covers; an uncovered action still runs.
• OpenCode is shim-based, not a native hook — treat its coverage as weaker than the five agents with verified pre-tool hooks until confirmed otherwise via beam agent list.
• Policy sync isn't instant — a tightened org policy needs a beam sync (or the service's own sync cycle) to reach a given machine; don't assume a dashboard change is enforced everywhere immediately.
• Requires enrollment for org policy — the standalone skills library works without a dashboard connection, but blocking/redaction driven by organization policy requires the device to be paired.
• Young, fast-moving product — command names, agent coverage, and the exact policy model have already changed once (Beam CLI → AgentBeam); re-check beam agent list and the docs after upgrades rather than assuming this guide's specifics hold indefinitely.

Quick start checklist

1. Run npm i @agent-beam/beam -g then beam setup — this detects agents, installs hooks, inventories MCP servers, pairs the dashboard, and starts the service in one step.
2. Confirm coverage with beam agent list and check the service with beam service status.
3. Open beam studio to confirm local activity is being captured.
4. Run beam sync after any organization policy change you need enforced immediately.
5. Copy security-assessment, skill-scanner, or mcp-scanner-style workflows from the Skills/ library into your agent for a deep manual review before trusting a new skill or MCP server.
6. If you manage a team, use the dashboard to block a risky MCP server org-wide rather than relying on every individual machine's local policy.

Related on explainx.ai

• WipeBench: 112 Docker scenarios for coding-agent safety

• Top AI Agent Security Platforms in 2026

• Why explainx.ai Is Building Sentinel

• What Are Agent Skills?

• MCP Security Guide

• Agent Skills Security Threats and Verification

• Claude Code Hooks: Automate Actions on Tool Calls

• Numbat: Perplexity's Agent Observability Tool

• Indirect Prompt Injection in AI Agents

Sources

• Beam CLI on GitHub — origin project, skills library, and historical CLI reference
• AgentBeam — current install docs, dashboard, and team/hosted policy setup
• @agent-beam/beam on npm — current install target for npm i @agent-beam/beam -g

This guide reflects AgentBeam's beam setup-based install flow, agent hook matrix, and dashboard features as described by the project as of September 18, 2026. Command names, policy defaults, and agent coverage are evolving quickly — run beam agent list and check the official docs after upgrading.