Organisations do not build every AI agent with the same framework.
Some use established agent frameworks. Others build custom orchestration, internal platforms or specialised runtimes. As AI agents become operational, the underlying technology can vary significantly while the organisation's governance requirements remain consistent.
FirstHelm provides a framework-agnostic control layer for autonomous AI agents. The core principle is: Build agents however you want. Apply consistent operational control around them.
FirstHelm is designed to work with custom agents through HTTP calls and webhooks, allowing teams to connect existing agent architectures without adopting a single underlying framework.
What Is a Custom AI Agent?
A custom AI agent is an autonomous or semi-autonomous AI system built using an organisation's own architecture rather than relying entirely on a standard agent framework. A custom agent might use:
- internal orchestration
- proprietary APIs
- custom Python or TypeScript code
- workflow engines
- model APIs
- internal tools
- specialised runtimes
- multiple AI models
Custom architectures can be useful when an organisation has unusual requirements. They can also make centralised governance harder.
Why Custom Agents Need a Control Layer
A custom agent may work perfectly from an engineering perspective while still lacking:
- central monitoring
- consistent constraints
- approval workflows
- intervention mechanisms
- operator permissions
- auditability
- autonomy management
The organisation may end up building these capabilities repeatedly. A control plane provides a shared layer.
Framework-Agnostic Agent Governance
The underlying agent technology should not necessarily determine how an organisation governs AI. A governance policy such as:
External communications above a defined risk threshold require human approval.
should apply whether the agent was built using:
- LangChain
- CrewAI
- AutoGen
- OpenAI Agents
- Anthropic
- a custom runtime
This is one of the key reasons to separate agent execution from agent governance.
How Custom Agents Connect to FirstHelm
FirstHelm's architecture supports custom HTTP/webhook integrations. The basic pattern is:
The agent can communicate relevant activity to the control layer. The control layer can evaluate applicable constraints and handle approvals or interventions. This allows an existing agent architecture to participate in a common governance model.
What Can You Control?
Custom agents can be governed using controls such as:
- budget limits
- forbidden actions
- approval gates
- rate limits
- time windows
- mission constraints
- agent constraints
This allows the organisation to standardise policies — including constraints — without rewriting the underlying agent.
Monitoring Custom AI Agents
A custom architecture should expose enough information for operators to understand what the agent is doing. Useful information includes:
- agent identity
- mission
- action
- status
- outcome
- cost
- risk level
- approval state
- constraint result
FirstHelm can provide a central operational view across connected agents.
Approval Workflows for Custom Agents
Approval workflows should be independent of the specific framework. For example:
If a proposed action exceeds the defined financial threshold, request human approval.
The same policy can apply to:
- a custom Python agent
- a LangChain agent
- an OpenAI agent
- an Anthropic-powered system
This prevents governance from becoming tightly coupled to one technical implementation. See Approval workflows in depth.
Intervention in Custom Agents
Custom systems often have their own execution lifecycle. A control integration should therefore define how intervention signals are handled. For example:
- Agent starts mission.
- Agent proposes action.
- FirstHelm evaluates constraints.
- Action passes or requires approval.
- Operator approves, rejects or intervenes.
- Agent receives the resulting instruction.
- Activity is recorded.
The exact implementation can vary. The governance model remains consistent — see intervention in depth.
Custom Agent Permissions
Custom agents should still follow least-privilege principles. The agent should have:
- only the tools it needs
- only the systems it needs
- only the data it needs
- only the financial authority it needs
A control plane can then add additional conditions around that baseline access — see least-privilege in depth.
Custom Agents and Audit Trails
Custom systems often have fragmented logs. One system records model activity. Another records API calls. Another records business actions. Another records human approvals.
A control plane can provide a common operational record linking important events. This is particularly valuable when organisations operate agents across multiple teams.
Custom Agents and Autonomy
A custom agent can participate in an organisation-wide autonomy model. For example:
This creates a common governance model even when the technical implementation differs — see the autonomy model in depth.
Custom AI Agent Governance Example
Consider a company with an internal operations agent built entirely in-house. The agent can:
- query internal systems
- create tickets
- update operational records
- trigger workflows
The company does not want to replace its custom architecture. Instead, it can connect the agent to FirstHelm and establish:
- mission limits
- action constraints
- approval thresholds
- operator roles
- monitoring
- interventions
- audit records
The internal engineering team continues to own the agent. The organisation gains a consistent control layer.
Why Framework-Agnostic Control Matters
Frameworks change quickly. Organisations may move from one framework to another as requirements evolve. If governance is embedded entirely inside the agent implementation, a framework migration can require governance to be rebuilt.
A separate control layer reduces that coupling. The governance policy remains stable while the underlying agent technology changes.
Custom Agent Control Plane vs Building Your Own
An organisation can build its own control plane. That may be appropriate for some highly specialised environments. However, building a full control layer can require significant functionality:
- agent registration
- identity
- mission management
- constraint evaluation
- approval workflows
- operator permissions
- intervention
- activity logs
- audit exports
- autonomy management
- dashboards
- integrations
A dedicated control plane can provide these capabilities as a shared service rather than requiring every agent team to rebuild them.
Custom AI Agent Control Checklist
Before deploying a custom agent, ask:
- How is the agent identified?
- How are missions defined?
- Which actions are allowed?
- Which actions are prohibited?
- Which actions require approval?
- How are permissions managed?
- How is activity monitored?
- How can a human intervene?
- How are decisions recorded?
- How is autonomy adjusted?
- How will the control layer interact with the agent?
- What happens when the agent becomes unavailable?
Frequently asked questions
Q: Can FirstHelm work with custom AI agents?
A: Yes. FirstHelm is designed to be framework-agnostic and can integrate with custom agents through HTTP/webhook-based connectivity.
Q: Do custom agents need to use LangChain or another framework?
A: No. The purpose of the framework-agnostic approach is to allow organisations to govern custom-built agents as well as agents built with established frameworks.
Q: Can custom agents use FirstHelm approval workflows?
A: Yes. Approval controls can be applied to actions that meet defined conditions.
Q: Can custom agents be monitored?
A: Yes. Relevant agent and mission activity can be sent to the control layer for central visibility.
Q: Can custom agents be stopped or redirected?
A: The integration can support intervention mechanisms such as pause, resume, redirect or termination depending on how the custom agent handles control signals.
Q: Why use a control plane for custom agents?
A: It separates governance from the underlying agent implementation and allows the organisation to apply consistent policies across different technologies.
Govern the Agent, Not the Framework
The future of agentic AI is unlikely to consist of one framework. Organisations will use different models, runtimes, frameworks and custom architectures. Governance therefore needs to operate above those implementation details.
FirstHelm provides that framework-agnostic control layer.
Build your agents your way. Govern them consistently. See the docs and pricing to get started.