How do we choose tools for a governed AI agent?

A useful tool decision begins with a task, not an integration list. Before we connect an AI agent to another system, we should decide what work justifies that access and which actions need a person involved.

For solo founders, small teams, nonprofits and creators, this is a practical planning exercise. We can distinguish between capabilities that support the intended work and capabilities that introduce responsibilities we are not ready to manage.

What work should each tool support?

We start by describing the agent's purpose in plain language. Then we ask how each proposed tool contributes to that purpose. If we cannot explain the connection, we can leave the tool out of the initial scope and revisit it later.

For example, when planning a research assistant, we might separate reading reference material from changing shared documents. Those are different kinds of authority, even when they involve the same workspace. We should document the intended action rather than treating access to a service as a single decision.

Agent Bob is a governance package builder by Master 22 Solutions, powered by ClawMaven. Its seven-step wizard covers purpose, personality, tools, autonomy, budget, memory and approvals. We can use those connected decisions to give tool selection context.

How much authority should we give a tool?

We should consider what a proposed connection could read, change, publish or spend. These are planning questions, not claims about integrations available in Agent Bob. The relevant capabilities depend on the platform where we build and run the agent.

For each intended action, we can ask whether the agent should act independently, prepare something for review or wait for approval. We should also consider what happens when a request falls outside the planned scope. A useful policy describes the boundary and the expected response to crossing it.

Budget and memory belong in the same discussion, as we cover in how we set agent budgets and action limits. A tool that incurs charges raises a spending question. A tool that handles information raises questions about what we want retained and for what purpose. We should resolve those choices alongside permissions rather than after setup.

Where will our tool limits take effect?

A documented limit is different from an implemented control. Agent Bob does not host or run agents, provide live monitoring, or enforce policy files by itself. A runtime must implement the policy and approval gates.

Before we rely on a tool restriction, we should ask how our chosen runtime applies it. We can plan tests for permitted actions, actions requiring approval and requests outside scope. These checks help us examine the implementation rather than infer behavior from the package alone.

SHA-256 integrity and HMAC signing do not prove runtime compliance. ClawMaven provides separate runtime governance. Likewise, mapping to NIST AI RMF, ISO/IEC 42001 or the EU AI Act is not compliance or certification. Our AI agent governance guide covers that distinction.

Which package fits our next step?

The builder is free, with no credit card required. Bob Free costs $0 and creates unsigned draft packages. We can begin there while working through the relationship between purpose, tools and authority.

Bob Pack costs $29 one-time per agent and includes a signed governance package. Expert review costs $99 one-time, includes Bob Pack and a written review of gates, tools and limits, with written fixes within 2 business days and no calls needed.

If we want help with implementation, Done for you starts at $497 one-time. It includes scope by email, a fixed quote, build, a governed pack, setup and testing on the client's platform, walkthrough and handover. The aim of our tool planning remains practical: describe the work, define the authority and identify where the runtime must apply the limits.