Pratik Patel 💸

this is honestly wild. we’ve been deep in the trenches building Poof V2 on the bleeding edge - * AI models * complex prompt orchestration * crazy agentic flows * massive context plumbing * onchain infra the goal was simple but brutal: build the most capable vibe-coding engine for onchain. and in the process... we built something much bigger 👀

Quick tip: give the agent an exit condition before the task starts. Stop after one working slice. Stop if the diff gets wider than expected. Stop if the proof is missing. A good stopping rule saves more time than a longer prompt.

Agentic payments are getting real. Visa is partnering with OpenAI for AI commerce. Mastercard launched Agent Pay for Machines. The hard part is not letting agents buy things. It is permissions, limits, disputes, and proof. What purchase would you let an agent make first?

When an agent gives you a result, what do you want to see first? 1. command output 2. diff summary 3. screenshot 4. cost and tokens 5. rollback note I trust the answer faster when the proof is shaped for review.

My current test for an AI workflow: can it survive the second run? First run proves novelty. Second run proves the process. Third run tells you what needs to be automated, logged, or killed.

Better prompts help once. Better runbooks improve the next hundred runs. The boring stuff matters: context, boundaries, proof, and a stop condition. I wrote the longer version because this is where agent reliability actually starts. What would you put in your first runbook?

I started writing tiny runbooks for agent tasks. Not big docs. Just: input budget allowed tools proof needed rollback path The funny part is that better agent work looks less like chatting and more like operations.

LG CNS just launched an agentic dev platform for enterprise systems: requirements, design, code, tests, QA, legacy migration. Useful signal. Vibe coding is not the end state. The product is context, specs, and verification around the code. What breaks first in your agent loop?

Hot take: the next serious agent product is not the one that feels most autonomous. It is the one with the cleanest operating loop. Start, observe, cap spend, verify, stop, resume. The magic demo matters less than the boring controls.

Tencent's WorkBuddy Enterprise launch is a useful agent signal. The hard part is shifting from super individuals to super teams. Agents need shared context, reusable skills, controls, and a human quality gate. Where does agent work become team infrastructure for you?

GitHub just made Copilot cloud agent callable from an API. That is the real shift. When a coding agent can be started by a script, portal, or release workflow, it stops being a chat tool and becomes build infrastructure. What would you automate first?

Quick tip: ask the agent for the smallest patch that could prove the idea. Not the final architecture. Not the full refactor. Not the beautiful generalized system. One useful slice, one visible behavior, one way to undo it.

Agent memory is becoming infrastructure, not a feature toggle. Walrus launched a portable, verifiable memory layer for agents across Claude, ChatGPT, Gemini, MCP, and SDKs. The real question is ownership. When an agent remembers your work, who controls the memory?

When you review AI-written code, which signal makes you slow down first? 1. diff touches auth 2. no test proves the risky path 3. summary sounds too confident 4. rollback is unclear I trust speed more when the stop signs are obvious.

Solana just made subscriptions a native payment primitive. That is boring in the best way. Recurring API billing, payroll, agent spending limits, and stablecoin invoices should not require every team to rebuild billing rails. What onchain product gets easier now?

My current agent prompt got better when I added a stop list. Stop before: touching auth changing money flows deleting data expanding scope guessing product intent The point is not less autonomy. It is putting the human at the expensive decisions.

Read more - pratik.pa.tel/blog/the-agent…

The IDE was the easy place for coding agents to start. The bigger shift is agents operating the loop around code: browser, terminal, screenshots, tests. I wrote about why that changes both productivity and risk. What would you let an agent touch outside the editor?

Read here - pratik.pa.tel/blog/ai-made-b…

I wrote about the security shift I think builders are underrating: AI made bugs cheap to find. The hard part is now triage, patching, and judgment. A scanner that outruns your response loop does not make you safer. It makes the backlog visible.

OpenAI and AWS just turned Codex into something enterprises can buy through the stack they already trust. That matters more than one more model dropdown. For agents, procurement, auth, billing, and governance are product features. What makes an agent usable at work?