The autonomy paradox
Why giving AI agents more freedom creates better control
I've spent the last few months knee-deep in building agentic AI systems, and I've discovered something that runs completely counter to everything we've learned as engineers: the instinct to tighten control when things go wrong? In the agentic space, you often have to do the exact opposite.
Let me explain why loosening the reins on your AI agents might be the key to building systems that actually scale.
The control trap we all fall into
Picture this: You're building an AI system with multiple agents. One handles user conversations, another does research, a third generates documents. Your first instinct? Create a detailed permissions table. Agent A can access tools X and Y but not Z. Agent B gets different permissions. You meticulously map out who can talk to whom.
We did exactly this on one of our project. An engineer created this comprehensive table laying out which agent had access to which tools, which memories, which bits of information. Something wasn't quite right, so we tweaked it. Added a permission here, removed one there. We argued about it. We iterated.
And we kept iterating. Because people can ask for anything in these systems, and we found ourselves constantly reprogramming which agent needs what for every possible flow.
Sound familiar?
The uncomfortable truth about scale
Here's what we discovered: trying to predefine every permission, every access pattern, every possible interaction creates a system that becomes impossible to scale. We can't predict every scenario. We can't hardcode every decision path. And most importantly – we shouldn't have to.
The solution feels wrong at first. Instead of tightening control, we give agents the ability to choose. Let them decide which tools they need. Let them determine which memories to access. Let them figure out the right approach.
I know what you're thinking. "But what about circular references? What about agents calling each other in infinite loops?"
We did not solve it by building rigid tree structures and breaking everything into branches and leaves. We solved it by adding a single line to the prompt: "Don't create circular references. If you've tried something three times and it hasn't worked, stop."
That's it. One instruction. The agent tried to fetch some data from RAG three times when the database was empty on local, then stopped. No complex control flow needed.
Memory: The secret ingredient
This approach only works because of memory – and I mean real memory, not just chat history. We're talking about four distinct types:
Immediate working memory: The volatile stuff during a single task Searchable episodic memory: Logs of past interactions the agent can search through Embedded semantic memory: Background knowledge and context External declarative memory: Databases, documents, ground truth sources
Without memory, you don't have agentic software. You have a fancy chatbot that needs constant hand-holding.
Consider this pattern we implemented: When users interact with our system, the agent watches for repeated attempts to get the same information. First time: "find me some experts." Nothing happens. "No, I need connections." Still nothing. "Find me the right people." Finally works.
With memory, the agent learns. Next time it might not even wait for the user to ask, it jumps straight to the right interpretation. No code changes needed. No prompt engineering. The system gets better through use.
The architecture flip
Traditional software architecture looks like a neat layer cake. UI on top, business logic in the middle, data at the bottom. Clean. Predictable. Controllable.
Agentic architecture scrambles everything. Your chat agent handles UI, but dynamically – context lives in prompts and memory, not hardcoded interfaces. Business logic distributes across planners, taskers, researchers, summarisers and other agents. Instead of calling predefined database queries, research agents decide what to query and when.
The integration layer becomes particularly interesting. We're moving from rigid API calls to dynamic tool selection. MCP (Model Context Protocol) enables this beautifully. Teams has an MCP now. So does Perplexity. Instead of API specific integrations, agents can request what they need in plain language: "Get me the latest project updates from Teams" or "Research competitor pricing strategies."
Learning to let go (with guardrails)
Don't get me wrong – this isn't about creating AI chaos. We still need boundaries. But the boundaries look different:
Give agents access to their own logs. When an agent can see what it's been doing, it can catch itself getting stuck. "Look at the logs and don't get caught in a loop" works better than trying to prevent every possible loop scenario through code.
Use confidence scoring. Let agents assess their own outputs. We use pseudo-confidence scores to trigger reflection or alternative approaches. Low confidence? Try a different strategy.
Enable depth control. Some questions need deep research. Others need quick answers. Let the agent estimate depth based on the task complexity rather than us predetermining it.
Implement reflection patterns. After generating a response, agents can critique themselves: "Evaluate this output for clarity, accuracy, and completeness." They catch issues we might miss.
The fear factor
I get it. Letting AI agents make autonomous decisions feels risky. What if they mess up? What if they waste resources? What if they do something unexpected?
But here's the thing: they will mess up. They will do unexpected things. That's exactly why we need evaluation frameworks, reflection loops, and LLM-as-judge patterns. Build the system, watch it go a bit nuts, then add targeted guidance through prompts rather than restrictive code.
The alternative – trying to predict and control every possible scenario – simply doesn't scale. You'll burn out your team trying to maintain it, and users will still find edge cases you never imagined.
Making it real
So how do you actually implement this? Start small:
Pick one area where you're constantly tweaking permissions. Maybe it's which agents can access which data sources.
Replace rigid rules with agent choice. Instead of "Agent A can access databases X and Y," try "Here are available data sources and what they contain. Choose what you need."
Add memory gradually. Start with simple episodic memory – just logging what agents do. Then add the ability to search those logs. Build from there.
Monitor and adjust through prompts, not code. When something goes wrong, resist the urge to add another permission check. Ask yourself: could a prompt instruction solve this?
Build evaluation loops. You need to know when agents are making good vs bad choices. This is where tools like Freeplay come in – run scenarios, catch issues early, iterate on guidance.
The payoff
When you get this right, something magical happens. Users stop hitting the same walls. The system handles edge cases you never explicitly programmed. Most importantly, it gets better over time without constant engineering effort.
Remember that example about users trying different phrases to find connections? After implementing memory-based learning, those friction points disappeared. Not because we coded every variation, but because the system learned what users meant.
That's the paradox: by giving up some control, we gain systems that are more controlled in their outcomes. By trusting agents to make choices, we build architectures that scale. By embracing uncertainty, we create better certainty for users.
The future of AI isn't about tighter constraints. It's about building systems smart enough to constrain themselves appropriately. And that starts with having the courage to loosen the reins.
cauri is building agentic AI systems at Artium.ai. This post is based on real experiences building production systems that learn and adapt.