Ibrahim El Khalil | Senior Software Engineer

Founder @ZYNIQ_SOLUTIONS | Building production-hardened AI agents that actually ship • 8y backend engineering | Turning complex AI into reliable systems

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

Building AI agents that survive production is harder than most people think.

8 years in backend.

Now focused on one thing:
Turning AI agents from demos into systems that actually ship.

At ZYNIQ we build agents that don’t fall apart under real load.

I’ll share what works every day.
Follow if you’re building for production.

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

Rate limits on external tools will eventually hit you in production.

An agent that ignores them just keeps failing and retrying.

Add backoff, respect the limits, and degrade gracefully when you hit them.
Ignoring rate limits is a fast way to get blocked.
#AIAgents

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

Caching tool results can save a lot of time and money.

Many agents re-call the same tool with the same input over and over.

Store recent results and reuse them when the input hasn’t changed.
Just make sure the cache expires when the underlying data can change.
#AIAgents

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@anothercohen Really !! That is going too far like the Idea.

[Alex Cohen @anothercohen]

This is an absolute masterpiece.

10/10.

No notes

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

Don’t let the agent decide its own next steps without limits.Unbounded planning looks smart in demos.

In production it often creates long, expensive, and unpredictable runs.

Give it a clear goal and a maximum number of steps.
#AIAgents

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

Version your agent prompts the same way you version code.

A small prompt change can completely alter behavior in production.
Keep a history of what worked, what broke, and why.

Treating prompts as throwaway text is how you end up debugging mysteries later.
#AIAgents

[Google Antigravity @antigravity]

Gemini 4 Carbon is live in Google Antigravity.

Built to explore thoroughly, reason deeply, and run extended verification loops, our strongest model for long-horizon agentic workflows is rolling out today in the desktop app, CLI, and IDE Extensions for Google AI Ultra subscribers and in Gemini for business. We are working on expanding access to more paid users soon.

Watch how we are using Carbon and Antigravity:

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@gaelmelen Lets connect and let know what are you building or into.

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@ray_zt Lets connect

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@Nehakathare https://t.co/ZSR6rXiAum

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

Timeouts on tool calls are not optional for production agents.

Without them, one slow external API can freeze the entire run.Set reasonable timeouts, handle the failure cleanly, and keep moving.

An agent that waits forever is worse than one that fails fast.
#AIAgents

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

Logging every agent step is useful until it isn’t.

Too much logging slows things down and creates noise.
Log the important decisions, tool calls, and failures.

Skip the low value intermediate thoughts unless you’re actively debugging.
#AIAgents

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@MarcosRGjr This is one of the solutions , stop it better then getting worse but even stoping an agent in production is still bad ...

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

The best AI agents in production are usually the boring ones.They do one clear task, have strict limits, and fail in predictable ways.

The flashy ones that can “do anything” tend to break in unexpected ways under real load.Simple and constrained beats flexible and fragile.
#AIAgents

[MarcRod @MarcosRGjr]

@Khalil_Preview 'Fail in predictable ways' is the whole game. We push it one step further: when a critical invariant breaks and nobody ACKs, the system fails stop with one pre-approved halt. No auto-repair. Would value your take: https://t.co/B9DkF7w3PO

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@miraonpages Claude Max

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@Nehakathare https://t.co/ZSR6rXiAum

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@miraonpages https://t.co/ZSR6rXiAum

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

SpaceXAI engineer, Lauren Tan:

"99% of people using GrokBot just for 1% of its real power. They run 1 agent without "loop" & "graph"

I'm running a team of 20+ GrokBot agents, fully autonomous. I have a Chief of Staff agent, a PM agent and 20+ workers - that's the new stack of engineer"

In a 1-hour session, a SpaceXAI engineer showed how to build a team of effective AI agents from scratch

this is worth more than a $500 agentic engineering course

watch this workshop today, then save it for later

[Ibrahim El Khalil | Senior Software Engineer @Khalil_Preview]

@Nehakathare https://t.co/ZSR6rXiAum