Most "AI agents" are demos: a prompt, a wrapper, a happy path. The hard part is everything that makes an agent trustworthy in the real world, state, memory, adaptation, and the judgment about when it should act on its own and when a human decides. This is how I think about that, built from a system that runs today.
Enterprises are full of work that is too messy to automate with rules and too repetitive to want a human doing it: calling another company, sitting through a phone tree, asking the right questions, and turning the answer into a decision. It is high-volume, low-joy, and it leaks money at every step. A rules engine cannot handle the variation. A chatbot cannot take the action. What is needed is an agent: something that can perceive, decide, act, and learn across a real, unpredictable process.
I built CompletionLoop, an autonomous agent that works a real operational process end to end. It places a live phone call, navigates the other side's interactive voice system, holds the conversation, extracts what matters, and drives toward an outcome, recovering revenue that would otherwise be lost. It is not a slide. You can watch it place a real call and build its understanding in real time.
The agent places a live call and turns the spoken conversation into a running transcript it can reason over.
It navigates the payer's IVR and the conversation, choosing what to ask and how to respond as the call unfolds.
It drives toward a concrete outcome, not a summary: identifying recoverable revenue and the path to recover it.
What it learns updates its memory of the counterparty, so the next interaction starts smarter than the last.
The interesting part of an agent is not the model call. It is the scaffolding that makes the model's output reliable, reusable, and safe to act on. Three ideas do that work here.
A typed graph that is the agent's structured memory of who it is dealing with, what they have said, and how they behave. It turns one-off calls into accumulating knowledge, so the agent gets better at each counterparty over time instead of starting cold every time.
The rules change mid-process all the time. Rather than hard-coding a script, the agent maintains playbooks it can revise as it learns what actually works, adapting its approach when the counterparty changes the game.
Perception is not enough; the agent has to reason toward a result. The appeal engine takes what the agent has learned and constructs the strongest path to the outcome, turning raw understanding into a defensible action.
The most important design decisions are about autonomy: where the agent acts on its own, and where a human stays in the loop. Getting that boundary right is what separates a product you can ship from a demo you can only show.
Anyone can wire a model to a prompt. The reason most agentic products stall is that they never solve memory, adaptation, and the judgment of when to act. I build for those from the start, because that is where agentic AI either becomes real or stays a demo.
This is not only a solo project. At Cisco, as Director of Product Management for Language AI on the Webex platform, I drove agentic AI infrastructure for building Webex AI agents, and shipped the LLM inference layer beneath it at a run rate of roughly 50 billion tokens per month, along with contact-center intelligence spanning millions of interactions. I have built agentic systems both with my own hands and as platforms that large enterprises build on. I know the difference between an agent that works in a notebook and one that holds up in production.
The through line goes back further. I am a named inventor on multiple granted U.S. patents in real-time communications, video, and collaboration, cited in 100+ later filings by Google, Amazon, Microsoft, Cisco, Zoom, Samsung, and IBM over more than a decade. Cisco cited that work across more than twenty of its own patents years before I joined the company.
A short version of the principles I build by:
An agent's value is what it does, not what it says. Design backward from the outcome.
State that accumulates across interactions is what makes an agent compound in value. Build it early.
Real processes change under you. Agents that revise their own approach beat brittle scripts.
Decide deliberately where the agent acts alone and where a human decides. That boundary is a product decision, not an afterthought.
I prototype by building the real thing, because the fastest way to understand an agentic product is to run it.
CompletionLoop is one agent that does a single job extremely well. Warden is the other half of the thesis: the platform that makes any agent safe to run in an enterprise. It is a working agent studio where every agent is governed by default. You give an agent instructions and grant it tools over MCP, and Warden classifies every tool by risk, runs reads on their own, and holds anything that moves money or changes state for a human to approve.
The judgment layer I keep returning to is the entire product here. A policy engine enforces spend caps, rate limits, and time windows. Every step lands on a hash-chained, tamper-evident audit log. Telemetry is redacted before it leaves the boundary, and cost is metered live with enforceable per-run budgets. It is the difference between an agent demo and an agent you would let touch production.
I want to lead product at a company betting on agentic, AI-native systems, as a VP of Product or Chief Product Officer. If that is what you are building, I would love to help lead it. francis.kurupacheril@gmail.com