Mayur Shah

Are you still shipping prompts instead of systems? Agents rarely fail for lack of IQ—they fail for lack of structure. Example: a payments ops team shipped a chargeback triage agent. In tests it looked brilliant. In production it stalled on edge cases, overused APIs, and repeated the same fix attempt. Not a model problem—a structure problem. The solution wasn’t a bigger model; it was a better pattern. Here’s the shift I’m seeing: from one-shot prompts to agentic patterns—explicit goals, tools, and control flow. A practical pattern you can apply this quarter: 1) Goal contract: Define inputs/outputs, success criteria, budgets, and stop conditions. Use structured outputs or tool calling to enforce shape (OpenAI: https://lnkd.in/eT-Msdtb; Anthropic tool use: https://lnkd.in/gp9iNBgH). 2) Tools with guardrails: Whitelist actions, add preconditions, idempotency, and rate limits. Keep a clear permission boundary (AWS Agents for Bedrock: https://lnkd.in/emJE3NDA). 3) Planner–executor loop: Move beyond chat to a state machine or graph with retries, timeouts, and breakpoints (LangGraph: https://lnkd.in/gPPGXEt3; Microsoft AutoGen: https://lnkd.in/g-3xnmxT). 4) Grounding and memory: Retrieve policies, docs, and past tickets with citations. Treat RAG and memory as first-class components (Vertex AI Agent Builder: https://lnkd.in/einzp8AA). 5) Feedback and observability: Add self-checkers, canary evals, and traces. Track cost, latency, and success against a test suite (see AgentBench for evaluation ideas: https://lnkd.in/e8ywbkdN). Where this works right now: - Cloud FinOps: An agent that detects cost anomalies, proposes changes, and opens a PR with rollback steps. - Support ops: A policy-aware triage agent that answers with citations, escalates with a ticket, and tags root cause. - SecOps: An incident summarizer that pulls logs, correlates with known IOCs, and drafts containment tasks. Predictions for 2025: - The unit of delivery moves from “prompt file” to “agent graph + policy + evals.” - Agent CI/CD becomes normal: every change runs tool simulations, safety checks, and task success benchmarks before release. If your agent isn’t performing, don’t start with the model. Start with the pattern. Structure turns “clever” into “reliable.” How are you moving from prompts to patterns? What’s the one structural change that improved your agent the most? Share in the comments and let’s compare approaches. Follow for more such content.#AI,#MachineLearning,#BusinessInnovation,#DigitalTransformation,#CloudComputing

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Banks keep saying they want to grow with small businesses, but the market isn't waiting for them to catch up. SMBs are already financing themselves with expensive credit card debt, paying 18-36% interest on money that a traditional loan could provide at a fraction of the cost. That's not a small gap. That's a $130 billion opportunity walking out the door in real time. And the longer banks rely on slow, outdated underwriting processes, the more ground they lose to fintechs and alternative lenders who are already moving at the speed SMBs need. Some numbers are hard to ignore, and this piece from nCino had quite a few of them.

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💳 Automated card issuing = Major cost-cutting win for loan providers For most lenders, the biggest operational costs don’t come from issuing credit or loans. They come from the workflows wrapped around it. Every disbursement, card activation, transfer issue, or support ticket adds friction. And at scale, that friction gets expensive while adding zero revenue. That’s why more lenders are shifting to embedded, automated card issuing with @Wallester White-Label. Not as a “nice-to-have”, but as a core efficiency strategy that: • Reduces manual handling • Shrinks support load • Prevents avoidable delays and errors • Keeps ops scalable as volume grows Find out more in our blog (🔗 in comments) #Wallester #WallesterWhiteLabel #Fintech #Lending #EmbeddedFinance

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Banks already have solid cores.
They’ve got APIs, compliance, and decades of reliability. What’s missing isn’t another system — it’s the intelligence layer that sits between intent and execution. When a customer says, “make sure I don’t overdraft this month,” the bank already knows: - Balances across accounts - Upcoming bills - Spending history - Available credit But nothing connects that knowledge to action.
Everything still waits for a human click. The intelligence layer would: - Monitor balances in real time - Predict cash flow gaps - Move money automatically within limits - Explain every action it takes It’s not about replacing bankers.
It’s about letting the infrastructure be as smart as the people who run it. The data’s there. The rails are there. The policies are there. What’s missing is orchestration. Where else in banking does knowledge exist but action doesn’t?

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Credit unions and community banks hold over $5.8 trillion in loans but have largely been shut out of securitization markets due to cost and complexity. 9Squid Private Markets changes that, an AI-powered platform that brings balance sheet modeling, loan securitization, and tokenization to institutions of all sizes.

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