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