In this blog post Building AI Agents with Claude vs ChatGPT Which Platform Gives More Control we will unpack what “control” really means in agentic systems, and how Claude and ChatGPT compare when you’re building AI agents for real enterprise work.

One pattern I keep running into is that leaders say they want “an AI agent”, but what they actually want is predictable autonomy. They want the model to act, but only within boundaries that align to risk, privacy, and business intent.

In Building AI Agents with Claude vs ChatGPT Which Platform Gives More Control, I’ll share how I think about control across the two ecosystems, based on what I’ve seen working (and failing) in real projects across Australia and internationally.

High-level first What is an AI agent really

An AI agent is just a language model connected to tools, memory, and workflows, with permission to take actions. The model provides reasoning and language. The tools provide capabilities like searching data, calling APIs, writing code, updating tickets, or generating documents.

The “agent” part emerges when you allow the model to plan across steps. Instead of answering once, it decides what to do next, uses a tool, evaluates the result, and iterates until it reaches an outcome.

The core technology behind control in agents

When people compare Claude vs ChatGPT for agents, the real difference is rarely raw model quality. Control comes from the platform mechanics around the model.

In practice, I break control into five layers.

• Instruction control: how reliably the system prompt and policies steer behaviour.
• Tool control: how tools are defined, invoked, constrained, and audited.
• Data boundary control: what the model can “see”, retain, and leak.
• Workflow control: how multi-step behaviour is orchestrated and tested.
• Governance control: logs, tracing, human approvals, and operational guardrails.

Both Claude and ChatGPT can build solid agents. The question is which ecosystem gives you more leverage in the layers that matter to you.

My definition of control for business-grade agents

For CIOs and CTOs, “control” usually means four things.

• Predictability: the agent behaves consistently under pressure and ambiguity.
• Least privilege: it can only access what it must, for only as long as needed.
• Auditability: you can answer “what happened?” without guessing.
• Intervention points: humans can approve, block, or roll back risky actions.

In an Australian context, this aligns nicely with how we already think about control: Essential Eight maturity, ASD guidance, and privacy expectations where “we didn’t mean to expose it” isn’t an acceptable outcome.

Claude vs ChatGPT Control comparison that actually matters

1 Tooling model Open standards vs integrated platform

Claude has leaned hard into an open connector approach with the Model Context Protocol (MCP). In plain language, MCP is a standard way to describe tools and data sources so the model can use them without every integration being a one-off.

What I like about this is the portability. When your tool layer is described in a common protocol, you can swap parts of the stack without rewriting everything.

ChatGPT (OpenAI’s ecosystem) has gone in a more integrated direction. In the API world, the Responses API and Agents SDK provide a coherent tool framework, with built-in hosted tools (like web search and file search) plus function calling for your own systems.

My take: if you value standardisation across vendors, Claude’s MCP direction is compelling. If you value a single cohesive developer platform with strong native tooling, OpenAI’s platform feels very complete.

2 Control over tool outputs Structured outputs and schema discipline

In enterprise agents, a surprising amount of risk comes from simple formatting failures. If an agent produces the wrong JSON, a downstream system can mis-route an incident, create the wrong change request, or populate the wrong customer record.

OpenAI has put a lot of emphasis on structured outputs and strict schema adherence in tool calling. When I need high confidence that a tool call matches a schema exactly, I generally find it easier to get deterministic behaviour here.

Claude’s tool use is very capable, and it’s improving fast. But I still treat schema adherence as something to validate, not assume. In other words, I plan for guardrails and retries.

If your agent is mostly “read and summarise”, this won’t matter much. If your agent is “create and execute”, it matters a lot.

3 Data boundary control Connectors, retrieval, and what the model can see

Both platforms can do retrieval-style patterns, where the model doesn’t need to memorise your data, it just gets relevant snippets at runtime.

The control question is: can you precisely scope what’s accessible, and can you prove it later?

With Claude’s connector approach, the permission model is often aligned to the underlying app permissions. That can be a strength, because it mirrors existing identity and access controls leaders already trust.

With OpenAI’s API tooling, you typically control data access by how you build your retrieval layer and what you pass into the model. That gives strong engineering control, but it also means you own more of the responsibility for “who can see what”.

In regulated Australian environments, I usually recommend designing retrieval like a security product. Assume the model will try to be helpful, and your job is to ensure “helpful” never becomes “leaky”.

4 Workflow control Orchestration, approvals, and tracing

An agent that can act needs an execution framework around it. This is where things move from “prompting” into “software engineering”.

OpenAI’s Agents SDK includes strong patterns for tool execution loops and tracing. Tracing is underrated. When something goes wrong in production, traces are the difference between quick resolution and a post-incident guessing game.

Claude-based agent stacks often end up being more “bring your own orchestration”. That’s not bad, but it shifts effort to your team. You’ll likely build (or adopt) your own run logs, step traces, and approval gates.

If you want a platform that hands you more of the operational scaffolding, ChatGPT/OpenAI tends to feel more opinionated and ready out of the box.

5 Safety and governance Control is also policy and culture

Control isn’t only technical. It’s governance.

In real organisations, the failure mode I see is not “the model hallucinated”. It’s “we let it do too much, too soon”. Leaders underestimate how quickly an agent becomes a shadow admin if you give it broad tokens, broad connectors, and no approval gates.

My rule: treat the first agent release like you treat a privileged access rollout. Minimal scope. Strong logs. Human approval for destructive actions. Tight change management.

A practical decision framework I use with tech leaders

If you’re choosing between Claude and ChatGPT for agent builds, I suggest starting with the control requirement, not the model preference.

• If you need strict structured outputs for tool calls and system interoperability, I usually lean toward OpenAI’s ecosystem first.
• If you want an open connector standard that can reduce vendor-specific integration debt, Claude’s MCP direction is worth serious consideration.
• If your risk posture is high (government, critical infrastructure, health, finance), prioritise tracing, approvals, and data minimisation over cleverness.
• If you’re early, optimise for speed of learning with a narrow agent and measurable outcomes, then harden control over time.

Real-world scenario An anonymised agent that almost became an incident

In one programme I worked on, the goal was simple: reduce time spent on recurring identity and access tickets. The agent was meant to triage requests, ask follow-up questions, and draft the right changes for review.

The first prototype worked brilliantly in demos. Then we tested it against messy reality: incomplete forms, contradictory requests, and “urgent” messages that bypassed process.

The risk wasn’t that the model was wrong. The risk was that it was confidently helpful in a way that could have produced unauthorised changes.

We fixed it by tightening control in three places.

• Destructive actions required approval: the agent could draft changes, but not execute them.
• Least privilege tool scope: it could read identity attributes needed for triage, but not browse across directories.
• Schema-validated tool calls: if the output didn’t match the expected structure, it didn’t proceed.

Only after that did we scale. That’s the order I recommend: control first, autonomy second.

Practical build steps that increase control regardless of platform

Whether you choose Claude or ChatGPT, these steps make an immediate difference.

1. Write your agent policy like a runbook. Include what it must never do, what it can do, and when it must ask a human.
2. Separate “think” from “act”. One component plans; a different component executes actions with hard checks.
3. Design tools as contracts. Small tools with tight inputs beat one giant “do everything” tool.
4. Validate every tool call. Schema validation, allow-lists, and explicit permission checks.
5. Log and trace every step. If you can’t explain the chain of actions, you don’t have control.

A small code example Tool calling with guardrails

This is deliberately simplified pseudo-code to show the shape of a controlled loop. The key idea is that the model proposes actions, but your code enforces policy.

// Pseudo-code: controlled agent loop
policy = {
 allow_tools: ["read_ticket", "draft_response", "lookup_user"],
 deny_tools: ["apply_change"],
 require_approval_for: ["apply_change"],
}

while (true) {
 step = model.next_step(context)

 if (step.type == "tool_call") {
 assert(step.tool in policy.allow_tools)
 validate_schema
 result = run_tool(step.tool, step.arguments)
 context.add(result)
 continue
 }

 if (step.type == "final_answer") {
 return step.output
 }

 // Safety stop
 if (too_many_steps() || low_confidence(step)) {
 return "Needs human review"
 }
}

That pattern sounds basic, but it’s the difference between an agent you can trust and an agent that creates hidden operational risk.

So which platform gives you more control

In my experience, ChatGPT/OpenAI tends to give you more platform-level control sooner, especially around structured outputs, hosted tools, and tracing-friendly orchestration.

Claude tends to give you more ecosystem-level control if you care about open connector standards and want your tool layer to be less vendor-specific over time.

The honest answer is that “more control” depends on which layer you want to own. OpenAI often packages more of the control mechanisms into the platform. Claude often encourages a more standardised connector approach, which can pay off as your agent estate grows.

Closing reflection

After 20+ years designing enterprise platforms, I’ve learned that autonomy is easy to demo and hard to govern. The organisations that succeed with agents are the ones that treat control as a first-class architectural requirement, not a feature request.

As agent capabilities keep accelerating, I think the winning architectures will look less like “one super-agent” and more like “many narrow agents” with explicit permissions, strong traces, and clear human checkpoints.