
Agentic AI —FAQ with Hilal Muhammad
Agentic AI —FAQ with Hilal Muhammad


Powering the Future with AI
Key Takeaways




How do we monitor and audit something that’s making decisions on its own? That’s the kind of question companies are asking now that agentic AI is moving from hype to real deployment. Hilal Muhammad lays out what these systems can do, where they fit, and what it takes to run them without losing control.
About Muhammad Hilal
Hilal Muhammad is Senior Software Engineer at CNTXT AI. He builds and scales contextually smart agentic systems for enterprises across GCC with a focus on security, compliance, and real workflow complexity. Hilal cares about making automation practical and safe so businesses can rely on it in daily operations.
Building better AI systems takes the right approach
FAQ
Traditional AI and RPA assume the world behaves in clean, structured steps. Anyone who has spent more than two weeks in an actual company knows that’s not real life. Things break for no reason, data goes missing, systems freeze at the worst moment, and half the process lives in someone’s head because nobody ever updated the documentation.
Agentic AI is built to handle that kind of chaos. It can reason through a task, adjust when something fails, pull the right info from different systems, and keep going without you writing a giant script for every possible scenario. Older tools weren’t built for that level of flexibility.
Speaking as a developer, most of the work we do is stitching together these messy gaps. And from the business side, that’s exactly where the real cost sits. Delays, exceptions, approvals buried in inboxes, tiny manual steps that everyone relies on but no one officially owns. That’s the stuff slowing everything down.
Agentic AI finally goes after that middle layer, half-structured work that’s been blocking real automation for years. It fills the gap between “the process on paper” and “the process as it actually happens.” And that’s the part companies have been waiting to automate forever.
RPA is basically “do exactly what I told you” and traditional AI is “tell me what you want predicted and I’ll spit out a number or label.” That’s it. They’re useful, sure, but they collapse the moment something doesn’t follow the script.
Agentic AI is task oriented. It decides how to complete something, not just what the output is. It can call tools, pull context, retry when it hits a dead end, and escalate when something smells off.
Anything that requires multi-step reasoning, decisions, and coordination across systems. Things like customer onboarding, financial operations, supply chain troubleshooting, field maintenance diagnostics, compliance reviews, procurement, contract workflows, or fraud investigations.
These aren’t single tasks but rather branching workflows with dependencies and incomplete information, the kind of work humans handle today because static logic trees can’t. Agentic AI brings reasoning and adaptability to that layer, finally making those complex, real-world workflows truly automatable.
AI agents work on top of your system. What I mean is that they connect through APIs, web interfaces, or even old-school data exports if that’s what you’ve got. The point is to make your stack usable and not rebuild it again.
Right now, we, humans, fill the gaps between tools by downloading, copying, checking, re-entering. Agents can do that type of orchestration work automatically. They don’t care if one app is cloud-native and the other still lives on a local server. As long as there’s a way to access or simulate interaction, they can bridge it.
Agentic AI acts more as a connective tissue between your fragmented systems. You get the impact of modernization without a full tech overhaul.
You give it boundaries. Every agent gets a role, a scope, a budget, and escalation rules. Nothing runs without guardrails. Actually nothing should run without guardrails. So we are looking at a combination of capability limits, access controls, logs, human-in-the-loop requirements, and kill switches.
No enterprise wants “autonomous agents” with free rein. You want “autonomous within well-defined lanes.” That’s the whole point. You’re putting structure around something that’s flexible by design so it stays useful instead of unpredictable. That’s the kind of control companies can live with.
The baseline is to log everything. Every step, every retry, every branch it takes needs to be recorded so you can see what happened without guessing. And you don’t treat the agent like a black box. It has to surface what it’s thinking, what inputs it used, and why it picked a certain path.
From there, you layer in checkpoints. Places where the agent stops and hands you a snapshot before moving forward so nothing runs off into a blind spot.
It’s the same idea we apply to real workflows: if you can see it, you can manage it. If you can’t, you spend your whole day chasing ghosts. Agents are no different in this case.
Oversight depends on the risk profile of the task. Low-risk tasks like scheduling or document routing can run fully autonomous. Medium-risk tasks need approvals at key checkpoints. High-risk tasks require full human review before execution.
You keep humans in the loop anywhere the risk is real. At the points where a bad decision hurts something you care about. The goal is to make sure the human stays in control of the outcomes while the agent handles the grunt work.
It’s the same classic security problems we already know, multiplied by a system that can move faster than humans notice if you don’t lock things down.
You’re giving a system the ability to do things inside your environment, so the stakes go up. If it’s allowed to touch real systems, then bad prompts, bad assumptions, or bad actors can push it into doing things you didn’t intend. And that means moving money, changing records, pulling data that shouldn't be pulled , or triggering workflows nobody approved.
Also agents only need certain permissions, so if you give them broad credentials because it’s easier, they become a single point of failure. One compromised agent and boom… your whole stack is exposed.
There’s also the social part where people tend to trust automated systems too quickly. So if the agent sounds confident, users might skip checks they’d normally do, which opens another door for mistakes.
We need a mental shift from “I do this work” to “I supervise and guide the system that does this work.”
The team needs to get comfortable with the idea that the workflow will change. Some steps disappear, some move around, and some become checks instead of full tasks. So there should be clarity on what that means for their day-to-day so we don’t feel blindsided.
And it's important to read the agent’s output and not blindly trust it. Like you have to verify the things that matter and let the rest run. Once that muscle forms, adoption stops being a problem.
I believe it is all tied to how quickly you give the agent real work. Once the agent settles into a workflow and stops bouncing issues back to humans or every time it handles a weird case without breaking, you get compounding value.
The early lift comes from removing the small steps that slow everything down. Stuff like collecting info, updating systems, fixing simple mismatches, or chasing missing data. When the agent takes that over, you start seeing time savings almost immediately.
You need a few people who know the work, know and own the systems, is would say:
- Senior Software Engineer / Architect Designs the overall system, database schema, and integrations, and ensures the product is stable and scalable, with a specialization in RAG for retrieval-based workflows.
- AI/ML engineers familiar with autonomous agents and prompt engineering
- Data engineers to maintain data pipelines and QA
- DevOps professionals for deployment, monitoring, and scaling
- Compliance officers for regulatory alignment and audit readiness
- Business analysts to translate needs into AI tasks and evaluate impact
- User experience designers to optimize human-agent collaboration
- Domain Experts (marketing, finance, operations)
















