Agent Control is live · free · last verified 25 Oct 2026 · viability monitor
Agent Control
Open-source agent control plane that enforces runtime AI agent policy at every step with an @control decorator
If your agents are live and you want policy checked at every step, Agent Control is the most credible open-source option we've looked at: the @control decorator means no rewrite, and the Control Store lets you keep the Bedrock, NeMo, Luna-2, Azure, and Cisco guardrails you already pay for. Apache 2.0 plus self-hosting answers the vendor-lock-in question procurement asks. The tradeoff is real — this is a developer-governance layer, so the console, user management, and SSO story is yours to build.
Verdict generatedVerified 5d ago · liveness 66/100 · cite: rightaichoice.com/tools/agent-control
- Platform teams standardizing one policy set across LangGraph, Strands, ADK, OpenAI Agents SDK, AutoGen, and CrewAI agents
- Security and compliance teams that need step-level guardrails plus audit traces of which control fired and why
- Engineering orgs that must self-host agent governance for latency or data-residency reasons
- Developers retrofitting runtime policy onto agents already in production without a rewrite
- Teams expecting a ready-made dashboard or clickable admin console on day one
- Buyers shopping for a low-code agent builder rather than a governance layer
- Organizations needing built-in user management or SSO without building it themselves
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Skip Agent Control if you need a ready-made dashboard, built-in user management or SSO on day one, or if you aren't running agents you can decorate in code — this is a developer-facing policy layer, not a clickable admin product.
Self-hosting means you pay for the compute that runs the Agent Control Server alongside your agents, plus the engineer time to run and upgrade it.
Agent Control is Apache 2.0 open source maintained by Galileo, so the software itself carries no license fee and self-hosting is your only infrastructure spend. That undercuts commercial guardrail platforms that charge per-seat or per-evaluation, but it shifts the cost to engineering: you own deployment, upgrades and support. Compare against hosted guardrail services only after counting the platform-team hours.
In short
Agent Control — Open-source agent control plane that enforces runtime AI agent policy at every step with an @control decorator. Best for Platform teams standardizing one policy set across LangGraph, Strands, ADK, OpenAI Agents SDK, AutoGen, and CrewAI agents, Security and compliance teams that need step-level guardrails plus audit traces of which control fired and why, Engineering orgs that must self-host agent governance for latency or data-residency reasons. Free to use.
What is Agent Control?
Agent Control — Open-source agent control plane that enforces runtime AI agent policy at every step with an @control decorator. Best for Platform teams standardizing one policy set across LangGraph, Strands, ADK, OpenAI Agents SDK, AutoGen, and CrewAI agents, Security and compliance teams that need step-level guardrails plus audit traces of which control fired and why, Engineering orgs that must self-host agent governance for latency or data-residency reasons. Free to use.
Agent Control is an open-source control plane for AI agents, maintained by Galileo under Apache 2.0, built for teams whose agents are already handling real traffic and need runtime policy rather than a hard-coded guardrail bolted onto one call path. The model is write once, deploy everywhere: policies live in one place and get evaluated at each step of a workflow, on LLM outputs and on tool calls, instead of only at the gateway. The integration surface is deliberately thin. You add the SDK, drop an @control decorator on a function, and call agent_control.init with an agent name and description. During execution the output goes to the Agent Control Server or gets evaluated in-process against every policy assigned to that agent, and the control comes back as allow, deny, steer, or warn. The SDK then executes the matching action, and a blocked call raises ControlViolationError you catch like any other exception. Policies are cached client-side so the per-step check stays cheap enough for interactive paths. Guardrails are pluggable, not baked in. A Control Store lets a mixed fleet route checks to Amazon Bedrock Guardrails, NVIDIA NeMo Guardrails, Galileo Luna-2, Azure AI Content Safety, or Cisco AI Defense, or to something you built yourself. Framework coverage includes LangChain / LangGraph, Strands Agents, Google ADK, OpenAI Agents SDK, AutoGen, and CrewAI, and audit logs record full workflow traces alongside which control fired and why. Use-case templates cover access, safety, security, compliance, accuracy, and tool controls, so you are not authoring a policy language from a blank page. Self-hosting in your own environment keeps checks local and keeps the policy set yours. Positioning-wise this is infrastructure for developers and platform teams; hosted guardrail services and gateway vendors will be an easier sell for anyone who wants a console first.
Key features
- Step-level policy enforcement on both LLM outputs and tool calls
- @control decorator adds governance to an existing function with one import
- Declarative policy language stored centrally in a Control Store
- Pluggable guardrails: Amazon Bedrock Guardrails, NVIDIA NeMo Guardrails, Galileo Luna-2, Azure AI Content Safety, Cisco
- Mix guardrails from multiple providers across a single agent fleet
- Automatic allow, deny, steer, or warn resolution driven by policy responses
- ControlViolationError raised in-app when a policy blocks a call
- Real-time policy updates applied without redeploying or restarting agents
- Client-side policy caching to keep per-step checks fast
- Remote policy handshake against the Agent Control Server, or in-agent local control evaluation
- Audit logs capturing complete workflow traces and which controls triggered
- Framework support: LangChain / LangGraph, Strands Agents, Google ADK, OpenAI Agents SDK, AutoGen, CrewAI
- Use-case templates for access, safety, security, compliance, accuracy, and tool controls
- Self-hosted in your own environment under the Apache 2.0 license
- Works with third-party agents and agent platforms beyond the supported frameworks
Behind the verdict
The reason to reach for Agent Control is step-level granularity. Gateway-level filters see a request and a response; an agent workflow has an LLM call, tool calls, an MCP hop, a summary, and a final action, and unwanted behavior usually originates somewhere in the middle. Putting the check on each decorated function is the right shape for that problem, and doing it with an import and a decorator is the cheapest retrofit we've seen for agents already in production. Where it earns its keep is heterogeneous fleets. If one team runs LangGraph, another is on Strands Agents, and a third is on CrewAI, a single Control Store that resolves allow, deny, steer, or warn the same way across all of them is worth more than three bespoke stacks of hard-coded checks. Client-side policy caching matters too: a per-step check that adds noticeable latency to an interactive agent gets removed by the first engineer who notices. We'd pass on this if you need a clickable administration product next quarter. There's no admin console in the pitch, and orgs that need built-in user management or SSO are signing up to build it. Non-technical stakeholders will not be authoring policies here either — this is a governance layer for developers and platform teams, and it assumes you can read a Python decorator. Low-code agent builders are a different purchase entirely. One caveat on scope: the guardrails are pluggable, which sounds like freedom until you realize the quality of your protection depends on the providers you wire in. Agent Control is the enforcement and policy layer, not the detector. Budget time to actually choose and tune those providers, and to write the policies that matter rather than the ones that are easy. Compare it against hosted guardrail services when you want someone else to
Limitations
- Agent Control is governance infrastructure, so it assumes you already have agents and the framework plumbing to attach it to.
- There is no admin console, no user management, and no SSO built in — teams that need those will build or buy them separately.
- Enforcement runs at each step, and although policies are cached client-side for faster execution and reliability, a policy check still sits in the request path, which matters for latency-critical pipelines.
- Non-technical stakeholders cannot author or review policies without engineering help.
- Because it is Apache 2.0 open source hosted in your environment, operational ownership, upgrades, and any support arrangements are yours.
- The published integration surface is limited to agent frameworks and guardrail providers, so check that your stack is represented before committing.
- The site presents the platform as early access.
as of 2026-10-15
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Is Agent Control still active?
Agent Control is live · free · last verified 25 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Agent Control? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Agent Control?
What people actually say about Agent Control — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Agent Control— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 25 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Agent Control 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed description, who should skip it, integrations, summary, our verdict, who it suits, features
- — changed switching out, who should skip it, pricing fit, hidden costs, features, description, switching in, setup time, our verdict
- — changed features, who it suits, description, summary, who should skip it, our verdict
- — changed our verdict, who it suits, description, features, hidden costs, integrations, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed who should skip it, our verdict, who it suits, summary, features, description
- — changed domain expiry, SSL certificate
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Agent Control cost in 2026?
Agent Control: Free to use. Pricing last verified 25 Oct 2026.
Where the pricing makes sense
The company stage and team size where Agent Control's pricing actually pencils out — and where peers do it cheaper.
Agent Control is Apache 2.0 open source maintained by Galileo, so the software itself carries no license fee and self-hosting is your only infrastructure spend. That undercuts commercial guardrail platforms that charge per-seat or per-evaluation, but it shifts the cost to engineering: you own deployment, upgrades and support. Compare against hosted guardrail services only after counting the platform-team hours.
Has Agent Control's pricing changed?
No plan price for Agent Control has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Agent Control?
Pick Agent Control if you are Platform teams standardizing one policy set across LangGraph, Strands, ADK, OpenAI Agents SDK, AutoGen, and CrewAI agents, Security and compliance teams that need step-level guardrails plus audit traces of which control fired and why, Engineering orgs that must self-host agent governance for latency or data-residency reasons, Developers retrofitting runtime policy onto agents already in production without a rewrite.
Look elsewhere if you are Teams expecting a ready-made dashboard or clickable admin console on day one, Buyers shopping for a low-code agent builder rather than a governance layer, Organizations needing built-in user management or SSO without building it themselves, Non-technical stakeholders who want to author policies without touching Python.
Skip it if Skip Agent Control if you need a ready-made dashboard, built-in user management or SSO on day one, or if you aren't running agents you can decorate in code — this is a developer-facing policy layer, not a clickable admin product.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Agent Control actually fits — and what changes day-one when you adopt it.
Adds the Agent Control SDK, imports control and ControlViolationError, decorates the existing LLM and tool-call functions with @control(), and registers each agent via agent_control.init with a unique agent_name.
Outcome: Every LLM output and tool call is checked against the agent's assigned policies, and a blocked step raises ControlViolationError the existing try/except handling already catches.
Assigns policies to each agent through the Control Store, routing PII detection to one provider and safety checks to another, then pulls the audit logs.
Outcome: The review pack includes full workflow traces showing which control fired at which step and why, rather than a summary of final outputs.
Writes one set of policies and applies them across a LangGraph orchestrator, a CrewAI crew and an AWS Strands agent, then edits a policy when an unwanted behavior appears.
Outcome: The change propagates in seconds without redeploying or restarting any of the three agents, keeping one policy source of truth across the fleet.
Use cases
- Blocking sensitive-data leakage when an LLM step tries to return a customer's SSN
- Validating what tools an agent is permitted to call before the call executes
- Redacting PII and secrets from agent responses at the step where they appear
- Producing audit trails that show which control fired, when, and why for a compliance review
- Running a mixed fleet — LangChain, CrewAI, Strands, ADK — under a single shared policy set
- Steering an agent mid-run when a policy returns steer rather than a hard deny
- Changing a policy in seconds to stop an unwanted production behavior without restarting agents
- Limiting agent access to sensitive database tables and internal tools
Setup time & first value
How long it actually takes to get something useful out of Agent Control — broken out by persona, not the marketing-page minute.
A single developer can get a first @control decorator running in minutes: import agent_control, wrap an existing function, and call agent_control.init with an agent_name. First real value — one policy enforced on a live agent with audit output — is realistically an afternoon for one engineer. Extending it to a mixed-framework fleet under one shared policy set is a multi-day platform project, and
Switching to or from Agent Control
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From hard-coded inline guardrails: replace per-function checks with an @control decorator and move the rules into the shared policy language.
- →From a gateway-level guardrail product: keep the gateway if you like, but add step-level @control checks so intermediate LLM outputs and tool calls are validated too.
- →From a single-vendor guardrail SDK: route existing checks through the Control Store so Bedrock, NeMo, Luna-2, Azure AI Content Safety or Cisco AI Defense can be mixed.
- →From no governance layer: start by decorating the LLM call and the highest-risk tool call, then expand coverage once the first policies are in git.
- ↗To a hosted guardrail service: if you need a vendor-run dashboard with support attached, expect to reimplement each policy in that vendor's format.
- ↗To a gateway-only guardrail: if step-level granularity proves unnecessary for your workload, a perimeter check is less code to maintain.
- ↗To a full agent platform: if you want policy bundled with building and hosting agents, consolidate onto one platform rather than two systems.
- ↗To in-house enforcement: because the license is Apache 2.0, you can fork and maintain the control plane yourself if you want to own the roadmap.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Agent Control”, and we withheld 6: 6 did not mention Agent Control. We are showing none, because we could not prove any of them are about Agent Control.
Official links
What are the best Agent Control alternatives?
The closest alternatives to Agent Control we list are Traccia, Agentsh and Aembit. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Traccia | Freemium | Pricing not published | — |
| Agentsh | Free | Free | — |
| Aembit | Freemium | Pricing not published | — |
Alternatives to Agent Control
View all
Tools that pair well with Agent Control
Common stack mates teams adopt alongside Agent Control, with the specific reason each pairing earns its keep.
Traccia
Traccia is an AI agent control plane that traces, costs, evaluates and enforces policy on agents you already run.
Agentsh
agentsh enforces syscall-level policy for AI coding agents — file, network, and process control that holds no matter what the prompt says.
Aembit
Identity control plane that brokers short-lived, policy-scoped credentials for AI agents, MCP servers, and non-human workloads.
Agent Control vs its closest rival
Agent Control's closest head-to-head is with Spider Cloud — If your primary need is obtaining structured web data for AI agents or RAG pipelines at minimal cost, Spider Cloud is the obvious choice—it’s battle‑tested, cheap, and now includes Browser AI commands. Agent Control is for a completely different problem: centrally governing and auditing multi-agent systems in production. Choose based on whether you need data extraction or agent safety, not on comparing apples to oranges. Verdict generated
Head-to-head comparisons
Agent Control vs Spider Cloud
If your primary need is obtaining structured web data for AI agents or RAG pipelines at minimal cost, Spider Cloud is the obvious choice—it’s battle‑tested, cheap, and now includes Browser AI commands. Agent Control is for a completely different problem: centrally governing and auditing multi-agent systems in production. Choose based on whether you need data extraction or agent safety, not on comparing apples to oranges.
GeneratedAgent Control vs Presto Voice
Choose Presto Voice if your primary need is automating drive-thru order-taking with upselling and proven ROI for QSR chains; pick Agent Control if you must govern, monitor, and secure AI agent workflows at scale. They solve entirely different problems, so your choice depends on whether your bottleneck is customer interaction throughput or agent runtime safety.
GeneratedAgent Control vs Temporal Ai
Choose Temporal AI if you need to build reliable, fault-tolerant AI agents that survive crashes and require durable execution with automatic state recovery. Choose Agent Control if you already have agent pipelines and need centralized runtime governance, policy enforcement, and observability across multi-framework deployments. Temporal is a platform for building; Agent Control is a platform for overseeing.
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