Claude Agent SDK
An agent that can act needs a boundary before it needs a prompt
Our engineers have shipped Claude into production with tool access, including MCP commitment tracking inside a multi-tenant coaching platform. The design question is never what it can do. It is what it must never do.
What you are getting
4 things that decide this
- 01Senior engineers who have put Claude to work with real tools in a live product, not a scripted demo.
- 02They define permissions, stopping conditions and cost limits as part of the build. An agent that loops without a ceiling is a bill and an incident.
- 03You interview each engineer before they start, and declining one costs nothing.
- 04Tool definitions, permission rules and the evaluation set are yours from the first commit.
What building on the Claude Agent SDK actually involves
An agent differs from a chatbot in one way that matters commercially. It takes actions, so a mistake changes something rather than just reading badly.
On Go4Gr8 the assistant tracks the commitments a leader makes during coaching, through MCP tools, inside a platform where every user has their own agent. The tool can write a commitment for that user and cannot reach another organisation's history, because the rule sits in the server rather than in the wording of a prompt.
That is the whole discipline. Decide the blast radius in code, give the agent the narrowest tool that does the job, and make every action it takes reviewable afterwards.
What they design before the agent runs
A stopping condition
A limit on steps, time and spend per task. Without one, a confused agent retries the same failing action until somebody notices the invoice.
Tools with narrow scope
A tool that books one appointment, not a tool that runs arbitrary queries. Narrow definitions are what contain the damage when the model is wrong.
Identity carried into every call
The agent acts as a specific user with that user's permissions, so it cannot quietly operate as an administrator.
Approval where it counts
Irreversible or costly actions pause for a person. Everything else runs unattended, which is the point of building an agent at all.
A record of every action
What was called, with what arguments, and what came back. After an incident this is the only thing that answers what happened.
- TaskWhat it was asked to do
- PlanSteps, with a ceiling
- Tool callNarrow, and named
- PermissionAllowed, or refused here
- ApprovalFor the costly ones
- LogEvery action, reviewable
Boxes four and five are your safety, and neither belongs in a prompt. A prompt is a request; a permission check is a rule.
Claude given tools in live products
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
How hiring works
- 01
Tell us what it must never do
A free call about the systems it reaches and the actions that would be expensive to undo. If a fixed workflow beats an agent here, we say so.
- 02
Meet the engineers
We shortlist people who have drawn a permission boundary in production, and you interview them against your own bar.
- 03
They embed
Your repository, your review process, your environments. One of our engineers owns the boundary design and answers for it.
- 04
They hand over
Tool definitions, the permission rules, spend limits and the evaluation set, with someone on your team trained to change them. Where a client wants the agent watched after launch, we stay on under a service level we agree.
Stack
Agent layer
Around it
Practices
Tell us the action you would not let software take
Bring the systems in scope and the mistakes that would actually hurt. The scoping call is free, and the permission model is the first thing we draw.
01What is the Claude Agent SDK for?
The Claude Agent SDK is Anthropic's toolkit for agents that run a loop: the model plans, calls a tool, reads the result and continues until the work is done or a limit stops it. It gives you the loop, the tool handling and the context management, so you do not write that scaffolding. You still own which tools exist, what they may touch, and where the agent runs.
02Do we need an agent, or would a fixed workflow do?
A fixed workflow is better whenever the steps are known in advance, because it is cheaper, faster and easier to test. An agent earns its cost when the path genuinely varies with the input and writing every branch is impractical. We will talk you out of an agent on the call if that is the honest answer.
03How do you stop it running up a large bill?
Ceilings set in code: a maximum number of steps, a spend limit per task, and cheaper models for the simple stages. Costs are tracked per task rather than as one monthly total, so an expensive pattern is visible in days instead of at the end of the quarter.
04Can it work with our internal systems?
Yes, and that is normally the whole reason to build one. Tools run inside your network or cloud account, so the agent reaches your systems through code you own rather than through direct database access. Go4Gr8 works this way inside a multi-tenant boundary.
05How do we know it still behaves after a model update?
You run an evaluation set against the new version before it goes live, checking the agent still picks the right tool with the right arguments on cases you care about. Model versions are retired on a published schedule, so this is planned work rather than an emergency.
06What shapes the cost of an agent build?
The number of tools, the systems behind them, and how reversible the actions are. An agent that only reads is straightforward. One that moves money or changes customer records needs approval steps, logging and far more testing. Scoping calls are free. Where we must work inside your existing codebase to answer, a paid two-week diagnostic produces a fixed price.
