Audit Your AI's Work. Every Time.
Vinay Patankar · 24 Mar, 2026 · Technology · Productivity
My four most-used prompts when working with AI agents have nothing to do with being clever. They're all some version of: "audit yourself." I use Claude Code to build and maintain the skills and processes that run my company. Hundreds of interconnected files. Rules that reference other rules. Defaults that cascade across systems. When I ask it to make a change, like updating a deck theme or rewriting a workflow, it does it. Fast. Confidently. Tells me it's done. I never take that at face value. Here's the loop I run every single time: "Audit all the changes you just made." "Make sure you've applied them everywhere." "Check for any conflicting or contradictory instructions." "Go back and confirm you've actually converted everything I requested." That last one is the kill shot. You'd be surprised how often the AI says "done" and then, when pressed, finds three more places it missed. A rule that contradicts the new one. A section it updated in one file but forgot the four other files that reference the same thing. An old default it left in place because it didn't think to look. AI is lazy in the same way people are lazy. It does 80%, declares victory, and moves on. Not maliciously. It just optimizes for completion over thoroughness. The fix is simple. Don't trust, verify. I learned how concrete that has to be when I caught an AI rubber-stamping a quality check instead of actually inspecting the work. I think about it the same way you'd think about checking a junior employee's first attempt at something important. The work might be 90% right. But the 10% it missed is where you get burned. The people getting the most out of AI right now aren't the ones writing elaborate system prompts. They're the ones who refuse to accept "done" at face value. They run audit loops. They push back. They make the AI prove its own work. 30 seconds of follow-up prompts saves 30 minutes of debugging later.
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Securing the Agentic Control Plane
Vinay Patankar · 23 Mar, 2026 · Technology
The Cloud Security Alliance just launched a new foundation at RSA 2025. One mission: "Securing the Agentic Control Plane." That is not a panel topic. That is a 501(c)(3) with dedicated funding and a single mandate. Three months ago, Forrester's Leslie Joseph formally defined the Agent Control Plane as a distinct enterprise software category. In February, Forrester polled 47 vendors. 79% recognized it as a real, standalone product category. Evaluation questionnaires go out in April. At RSA 2025, the pieces showed up everywhere. Geordie AI made the Innovation Sandbox finals with an agent security governance platform. Token Security made the finals with agent identity lifecycle management. Cisco extended Zero Trust Access to AI agents. Okta ships Auth for AI Agents in April. CrowdStrike paid $740M for SGNL to get dynamic agent authorization. Everyone is building a piece of the control plane. Nobody has the whole thing. The architecture has four layers: agent registry (what agents exist), policy enforcement (what they're allowed to do), runtime monitoring (what they're actually doing), and compliance reporting (proving it to auditors and boards). That compliance reporting layer is exactly why healthcare AI agents need proof infrastructure before they need more autonomy. Geordie AI does monitoring. Token Security does identity. Zenity does runtime detection. WitnessAI does usage visibility. Each one covers a layer. None spans all four. This is structurally identical to what happened with cloud computing. AWS built CloudWatch for AWS. Azure built Monitor for Azure. GCP built Operations for GCP. None of them built tools to manage multi-cloud environments. Datadog did. Worth $20B+. The same thing is happening with AI agents. Anthropic will build governance for Anthropic agents. OpenAI will build governance for OpenAI agents. Microsoft just priced Agent 365 at $15/user/month, and it only governs Microsoft agents. The vendor-neutral governance layer that works across all of them does not exist yet. Forrester is evaluating in April. CSA just formed a foundation. The Innovation Sandbox finalists are building fragments. The category is real. The race is open. Who's building the full stack?
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Jensen Huang Just Described My Exact Setup on the All-In Podcast
Vinay Patankar · 22 Mar, 2026 · Technology
Jensen Huang just described my exact setup on the All-In Podcast. I don't think most people caught what he actually said. He wasn't talking about chatbots. He was describing a computer. Memory. Skills. Resource management. Scheduling. I/O. An API that runs applications. Those four elements, Jensen said, "fundamentally define a computer." I rewound that part. Twice. Because he's not being philosophical. He's being literal. We now have, for the first time, a personal AI computer. Open source. Runs everywhere. Jensen laid out three inflection points over the last two years. ChatGPT made generative AI accessible to everyone. Grounded models and reasoning (o1, o3) made it useful enough to drive real revenue. Then agentic systems, Claude Code first, OpenClaw second, made the culture realize what an AI agent actually is. But the third one is different from the first two. ChatGPT and grounded models were improvements to the same thing. Agentic systems are a new category entirely. When your AI manages its own memory, runs cron jobs, spawns sub-agents, decomposes tasks, connects to external services, and exposes an API for running what Jensen calls "skills," that's not a tool anymore. That's a computer. It is the same shift I meant when I wrote that a coding agent is not a coding tool. I've been building exactly this. A personal AI system with long-term memory, a skills library, scheduled jobs, I/O to Slack and Discord and Gmail, task decomposition, agent spawning. It runs my morning operations, triages my inbox, preps my calls, drafts my content, iterates my decks. All autonomously. Hearing Jensen describe the same architecture on All-In to Chamath, Sacks, and Friedberg validated the whole thesis. The part that should make every founder pay attention: Jensen also said agentic software has access to sensitive information, can execute code, and can communicate externally. All three at once is dangerous. Governance is the real product problem now. Not building the AI computer. Building the controls so you can actually trust it. One more thing from the episode. Jensen said if a $500K engineer isn't consuming at least $250K worth of tokens, he'd be "deeply alarmed." If it was only $5K: "I will go ape." That's NVIDIA's CEO telling you tokens are not a cost. They're leverage. We're not in the "AI tool" era anymore. The shift already happened. Most companies just haven't noticed yet. What are you building with agentic systems? Not the chatbot wrapper. The actual computer.
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A Coding Agent Is Not a Coding Tool
Vinay Patankar · 19 Mar, 2026 · Technology
Everyone calls Claude Code a coding tool. That framing is too small. What it actually is: a self-building operating system. Not for your computer. For you. Think about what Windows or Mac actually is. It's a layer built on top of the command line so non-technical people can use a computer. You click, it translates. The raw complexity disappears behind the interface. Claude Code is doing the same thing. But instead of building one interface for everyone, it builds a custom interface for you, specifically. Based on how you work, what you care about, and the decisions you've already made. Every time you use it, it gets more configured to you. You tell it once how you like your emails formatted. You document how you want your calendar managed. You explain the exception you always make on Fridays. It reads all of it. Then it writes its own notes. Builds its own skills. Starts anticipating the next decision. At some point it stops being a tool you use and becomes a system that runs around you. That is why I stopped buying disconnected AI tools and started using one coding agent to build the rest of the system. I have over a hundred custom skills built up in my setup now. For how I review finances. For how I draft investor updates. For how I run triage on my inbox each morning. For how I prep for calls. Each one reflects a judgment call I made once about how I want something done. I didn't have to teach any of it twice. It just knows. And here's what's strange about that: the longer you run it, the more accurate it gets. Not because it was trained on more data. Because it was trained on more of you. Your decisions. Your preferences. Your exceptions. Your patterns. The old model of software: you climb a learning curve, reach a plateau, stay there. This is different. The system keeps building itself around you every time you use it. We called these things coding assistants because the first thing they were obviously good at was writing code. But that name undersells what they actually are. A second brain is the closer analogy. But even that isn't quite right. A second brain stores things. This builds things. Specifically, it builds a custom operating system for your work and your life, based on how you actually do things. No one has installed the same one twice. That's what makes this moment kind of strange and exciting. We're not adopting a new productivity app. We're not switching project management tools. We're at the beginning of a period where everyone who bothers to set this up properly gets their own custom OS. One that learns how they want to run things and just runs them. The people who do this early are going to have a compounding advantage that will be hard to explain to the people who didn't.
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I Audited 10,814 Financial Transactions in One Afternoon With an AI Agent
Vinay Patankar · 15 Mar, 2026 · Technology
I audited 10,814 financial transactions yesterday. Every single row. It took one afternoon. Not me personally. An AI agent I built. Here's the backstory. I'm a CEO. I am not an accountant. But I run a SaaS company, and every month our finance team sends me a financial package. Income statement, burn report, balance sheet. I always read it. I never question it. Because what am I going to do, go through 24 months of QuickBooks line by line? Yesterday I did exactly that. I connected my AI coding agent to our QuickBooks API. Pulled every transaction from the last 24 months. 10,814 rows. Purchases, bills, journal entries, vendor payments. Then I had the agent review every single row against five checks: is it categorized correctly? Is the class assignment right? Is there supporting evidence? Are prepaid amortizations tracking? Are clearing accounts clean? That only works if the agent's output gets treated as evidence to inspect, which is why I keep saying: audit your AI's work every time. The results were not what I expected. 8,494 rows cleared. Clean. 1,888 rows flagged for triage. Missing metadata, ambiguous categories. 56 rows need supporting evidence that doesn't exist in the system. 376 rows are confirmed issues. Wrong classifications, clearing account residue, prepaid amortization gaps, and transactions with no class assignment at all. The February 2026 financial package our team posted? It doesn't reproduce from the current QuickBooks ledger. The cash and prepaid balances don't match. I would have never caught that by reading the PDF. Here's the thing. This wasn't some enterprise financial audit tool. It was a Python script that an AI agent wrote, connected to the QuickBooks API, running checks I described in plain English. Total cost: about $3 in API calls. The script took 20 minutes to build. The audit ran in under 2 hours. The findings would have taken a human analyst days to produce, and they still would have missed the pattern-level issues because nobody reviews 10,814 rows manually. This is the part of AI that doesn't get enough attention. Not the chatbot answering customer questions. Not the copilot drafting your emails. The agent that quietly reviews your entire financial ledger and tells you what your finance team missed. Most CEOs trust their numbers because they don't have the time to verify them. That's not a trust problem. It's an access problem. And AI agents just solved it.
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I'm at a 45,000-Person Conference and My AI Second Brain Is Running My Company Back Home
Vinay Patankar · 13 Mar, 2026 · Technology
I'm at HIMSS this week. 45,000 people. Three days of back-to-back sessions, hallway conversations, and vendor meetings in Las Vegas. My company is running without me. Not because I have a huge team covering for me. Because I built a system that does it. ## The 5 AM Operating System Every morning at 5 AM ET, before I wake up in my hotel room, a 17-step operating system kicks off automatically. It pulls my call recordings from yesterday. Scans my calendar. Runs a company pulse check across Slack, email, and CRM. Enriches any new contacts in our CRM. Triages both my inboxes. Preps me for today's calls. Summarizes everything that happened overnight across every channel. Reviews the sales pipeline. Scans industry news. Generates content ideas. Processes my task backlog. Pulls business metrics. Flags relationships I haven't touched in a while. Audits whether I followed up on last week's meetings. Plans my day. Then it compiles all of it into a single daily brief that's waiting for me when I open my phone. By 6 AM, before I've had coffee, I know exactly what happened, what matters, and what to do first. ## What Happened While I Was on the Conference Floor That's the morning. Here's what happened while I was walking the HIMSS floor on Monday. My system iterated a sales deck from v6 to v9. Four versions in one day. Fixed margins, updated slide content, improved centering. Uploaded each version to Google Drive and posted it to our internal channel for review. It rewrote 12 marketing documents to match our new positioning. Pricing pages, FAQ, competitive analysis, proposal templates, ICP profiles, messaging frameworks. All consistent. All updated in parallel. It ran a full LinkedIn content analysis across 62 published posts and a year of analytics data. Identified that customer case studies with specific numbers outperform everything else by 3x. Documented 14 improvement ideas for our content system. It processed 292 emails across two inboxes. Classified every message. Archived what didn't matter. Created task files for things that needed action. Both inboxes hit zero. I didn't touch any of it. I was in a session about AI agents in clinical workflows. ## The Conference Anxiety Problem Here's the thing nobody talks about at conferences. The CEOs walking around aren't fully present because half their brain is worrying about what's piling up back at the office. The inbox growing. The Slack messages stacking. The decisions waiting. I stopped worrying about that months ago. ## How It Works The system isn't magic. It's an Obsidian vault, Claude Code, a handful of API integrations, and a lot of carefully written skill files that tell the AI exactly how to do each job. The reason that works is the same reason a coding agent is not just a coding tool: it becomes infrastructure around the way you operate. It took months to build. It breaks sometimes. I fix it and it gets better. But the compounding effect is real. Every skill I add makes the next one easier. Every morning pulse run catches things I would have missed. Every triage cycle keeps the noise from turning into chaos. ## Tool vs. Infrastructure I'm not saying every CEO needs to build this. I'm saying the gap between "CEO who uses AI tools" and "CEO whose company runs on AI infrastructure" is getting wider every month. At HIMSS, I watched vendors pitch AI copilots that help with one task at a time. Summarize this note. Draft this email. Answer this question. That's helpful. But it's not the same as a system that wakes up before you do, runs your entire operating rhythm, and hands you a brief that says "here's what happened, here's what matters, here's your plan." One is a tool. The other is infrastructure. I know which one I'd bet on.
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Healthcare Compliance Is Where AI Agents Earn Trust or Lose It Forever
Vinay Patankar · 11 Mar, 2026 · AI
I sell AI automation to hospitals. I spent three days at HIMSS in LV. And honestly? I'm less confident about AI agents in healthcare than when I arrived. Not because the technology is bad. Epic just launched Agent Factory. Microsoft announced Agent 365 at $15 a seat. Every booth had some version of "our AI does the work for you." The demos were good. The compliance conversations were terrifying. I kept ending up in sessions where compliance officers and risk managers were asking the same question in different ways: "Who is responsible when the agent makes the wrong call?" Nobody had a great answer. Trust in healthcare AI isn't about whether the agent can do the task. It's about whether you can prove it did the task correctly, every time, to someone who will fine you if you can't. That's a fundamentally different bar than "it works." That is the regulated-industry version of the control plane problem. A STAT News piece that came out during the conference kind of nailed it: health AI agents are here but validation is not. The capability gap closed. The trust gap didn't. I watched a vendor demo an agent that could process prior authorizations in minutes instead of hours. Legitimately impressive. Then someone in the audience asked "where's the audit trail?" and the demo person kind of froze. There was no audit trail. The agent just did the thing. In healthcare, "it just did the thing" is not an acceptable answer. Joint Commission doesn't care how fast your agent works. They care whether you can produce documentation showing every step, every decision, every exception, and every human review point. The companies getting this right are the ones that started with the compliance architecture and added AI on top. Not the other way around. They built the evidence layer first. Who approved what, when, why, what data was used, what the fallback was if the agent was wrong. The ones getting it wrong are bolting agents onto existing workflows and hoping the existing audit trail covers it. It doesn't. An agent doing work autonomously generates completely different compliance requirements than a human clicking through screens. We see this constantly at Process Street. The organizations that deploy AI agents successfully in regulated environments are the ones that treat compliance as the first design constraint, not the last checkbox. They build the proof infrastructure before they build the automation. Most companies are still doing it backwards. Ship the agent, worry about compliance later. That works fine until survey season. If you're deploying AI agents in healthcare or any regulated industry, the question isn't "can the agent do this?" It's "can we prove to a regulator that the agent did this correctly, and what happens when it didn't?" That's not a feature request. That's the whole product.
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The Agent Boss Is Coming
Vinay Patankar · 09 Mar, 2026 · Technology
I was at DocuSign Partner Day in Las Vegas. A Microsoft exec named Edwin Bargas gave a talk and one thing he said kind of stuck with me. His argument was basically that we're going from copilots to agent teammates to fully agent-operated teams where the human is just the manager. He called that role the "agent boss." All three phases in the next one to three years. He admitted it sounds like science fiction. Then pointed out that everybody said that about AI too, and here we are. Hard to argue with that. Now look. I know "agent boss" sounds like something LinkedIn invented to sell courses. But the actual idea underneath it is real, and we're kind of living it at Process Street right now. We have AI agents running actual processes. Not summarizing documents. Not answering questions. Doing the work. And someone has to make sure they don't mess it up. Turns out that someone needs a completely different skill set than a traditional manager. You don't motivate agents. You don't do 1:1s. You don't coach them through a rough quarter. (They don't have rough quarters. They have bugs.) But you absolutely need to monitor their output, catch their mistakes, and know when to step in. That monitoring layer is why audit loops are becoming a core management habit for AI work. Managing people is inspiration, delegation, development. Managing agents is auditing, configuring, and supervising systems. Less team lead, more air traffic controller. Nobody is training for this yet. Every management book, every leadership course, every MBA program assumes your direct reports are humans. That assumption has about 18 months left, maybe less. The companies that figure this out first won't win because their AI is better. They'll win because their supervision infrastructure is better. The workflows that let a human stay in control when 90% of the work is automated. We have agents doing real task work right now. Phase 2 isn't coming. It's here. Most companies just haven't noticed because they're still arguing about whether to buy a copilot. The question isn't whether agent bosses will exist. It's whether your org will have any idea what to do with them.
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The 47 Clicks Between Patient Intake and Chart Update
Vinay Patankar · 08 Mar, 2026 · Technology
I've been going to a lot of healthcare conferences this year. Every keynote is about AI. Every booth has a copilot demo. But you know what actually stuck with me? Something I saw during a customer implementation. A nurse at a check-in station clicking through 47 screens to move a patient from intake to chart. Forty-seven. I counted. She wasn't slow. She was fast. Muscle memory fast. She'd done this thousands of times. Tab, click, copy, paste, switch system, re-enter the same allergies she just typed in the other system. The whole thing took eleven minutes. Nobody at the conferences I've been to was talking about those eleven minutes. They were talking about AI-powered diagnostics. Clinical decision support. Ambient listening that writes your notes for you. All real. All important. But all of it assumes the underlying workflow works. It doesn't. The dirty secret of healthcare IT is that most of the pain isn't clinical. It's operational. It's the 47 clicks between patient intake and chart update. It's the compliance officer chasing vendor certifications through email chains. It's the credentialing team manually verifying the same documents across three systems that don't talk to each other. These problems aren't sexy. No one puts "we eliminated 30 redundant data entry fields" in their conference booth headline. But that's where the hours are. It is also why healthcare AI agents have to earn trust through workflow evidence, not demo polish. We've seen this pattern across 1,000+ companies at Process Street. The teams that get the most out of AI don't start with the flashy stuff. They start with the workflow nobody wants to own. The one where someone says "oh yeah, that's just how we do it" and everyone nods and moves on. That's the process you automate first. The real AI conversation in healthcare isn't "will AI replace clinicians?" It's "will AI replace the 47 clicks between intake and chart update?" That second question is less dramatic. It's also worth about 10x more.
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My AI Second Brain Already Made Me $4,000
Vinay Patankar · 05 Mar, 2026 · Technology
Most people accept the first offer from their insurance company. I used to be one of them. My garage flooded last month. Six feet of water. Submerged my Tesla, completely bricked. Wetsuits, surfboards, electronics, furniture. Everything in storage, destroyed. The insurance company sent their offer. I was traveling. I had a few days to respond. The number looked reasonable enough. My instinct was to just sign it. That's the play, right? They know you're busy. They know you're not going to spend your weekend pulling receipts and researching comparable claims. So they send you a number that feels close enough, and you take it. I almost did. Instead I sent it to something I've been building for the last few weeks. An AI agent connected to all my personal data. My emails, my purchase history, my documents. I asked it: "Is this claim fair?" It told me no. Then it showed me why. It pulled comparable claims for similar losses. It found my original purchase receipts buried in Gmail going back years. Then it drafted a counter offer with all of that as supporting evidence. I read through it, hit send, and moved on with my day. The result was an extra $4,000. Not because I'm a great negotiator. Not because I spent hours on research. Because I had an agent that doesn't skip the fine print, doesn't lose track of old receipts, and doesn't just accept the first number because it's "close enough." Insurance companies have always had the information advantage. You're one person with a flooded garage and a lot on your plate. They do this thousands of times a day. Now you can have an agent that levels the playing field. For another Technology angle, read Evernote for Spreadsheets.
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Top 10 Compliance Software Platforms
Vinay Patankar · 12 Nov, 2025 · Business-process-management
The compliance software market has shifted away from static documentation tools toward platforms that operate directly within the workflow. Regulators expect more substantial evidence, boards want clearer visibility into risk, and operations teams must continue to move forward while maintaining control. These pressures have expanded the range of software solutions available, from enterprise GRC systems to lightweight operational platforms. This overview examines ten prominent vendors and how they fit into the modern compliance stack, ordered around the practical question of which systems best connect policy, control, and daily execution. ## Process Street Process Street is a platform that combines GRC and Operations into a single compliance operations suite. The platform enables organizations to transform policies into live workflows, integrating controls into day-to-day operations. As tasks are completed, the system automatically captures timestamps, data, approvals, and evidence. This creates a detailed audit trail without requiring manual assembly. The product combines governed documentation, workflow automation, and an agentic AI layer that checks tasks against policy and highlights exceptions. It is used across various industries, including financial services, real estate, healthcare, manufacturing, and the public sector, to manage compliance, employee onboarding, due diligence, internal reviews, recurring control activities, and policy attestations. The central idea is to close the gap between written standards and actual execution. Policies can be updated in one place, linked directly into workflows, and monitored across teams. This positioning makes the platform appealing to organizations that want consistent, auditable operations without the overhead of a heavyweight GRC suite. ## Vanta Vanta is widely recognized for its focus on security and trust management. It automates evidence collection for certifications such as SOC 2 and ISO 27001 by pulling data from cloud infrastructure, identity providers, and other systems. High growth technology companies and fintechs use Vanta to accelerate initial certification and maintain continuous compliance with minimal manual work. The platform excels in technical control monitoring, though broader operational workflows typically sit outside of it. ## Drata Drata operates in a similar segment focused on security and privacy compliance. The platform consolidates controls, risks, and evidence for frameworks including SOC 2, ISO 27001, and HIPAA. Automated checks and integrations keep compliance data current while reducing reliance on spreadsheets. The system works well for organizations that must manage multiple overlapping security frameworks, although complex business workflows usually run in other tools. ## OneTrust OneTrust is one of the largest vendors in the privacy and data governance space. Its platform covers privacy programs, consent management, data discovery, AI governance, and integrated risk. Enterprises utilize OneTrust to coordinate compliance across various regions and regulatory frameworks. The system supports policy management, regulatory mapping, audit functions, and risk assessments. Its scale and breadth are suitable for organizations with complex compliance needs, though implementations can be lengthy and require specialist attention. ## Diligent One Diligent One focuses on governance and senior-level oversight. It brings together board management, ESG data, risk registers, and audit activity. Directors and executives rely on Diligent for consolidated reporting and governance visibility. The platform is structured around top level risk and compliance oversight rather than operational execution. Evidence and workflow activity typically originate from other systems and are integrated into Diligent through updates. ## NAVEX One NAVEX One is a broad ethics and compliance platform that includes policy management, training, incident reporting, risk management, and third party due diligence. NAVEX has long been associated with hotline and whistleblowing solutions and has expanded into integrated risk. It suits organizations that need a global ethics and compliance program with structured frameworks and training content. Smaller teams with narrower operational needs often find the platform more extensive than necessary. ## ServiceNow GRC ServiceNow GRC is built on the larger ServiceNow platform and connects risk and compliance processes with IT service management and security operations. It supports control testing, exception workflows, risk assessments, and continuous monitoring. Organizations that already rely on ServiceNow can extend the platform to cover compliance and risk functions. For companies without a strong ServiceNow footprint, the required configuration and licensing can feel heavier than more focused alternatives. ## MetricStream MetricStream is a leading provider of enterprise GRC solutions. Its platform integrates enterprise risk, regulatory compliance, internal audit, and cyber risk into a single system. It is commonly used by large enterprises with complex governance structures, especially in financial services, energy, and manufacturing. MetricStream supports structured workflows and global reporting but can require substantial implementation effort. ## LogicGate Risk Cloud LogicGate Risk Cloud is a no code GRC platform that prioritizes flexibility. Organizations can configure custom workflows, data models, and approval paths, allowing them to tailor processes without significant engineering work. This makes it useful for mid-sized firms migrating away from spreadsheet-based compliance. The flexibility places more responsibility on internal teams to design and maintain processes. ## Hyperproof Hyperproof is a continuous control management platform for security and privacy frameworks. It helps organizations manage SOC 2, ISO 27001, NIST, and PCI from a single system and reuse evidence across frameworks. Controls can be maintained through scheduled tasks and dashboards that track compliance health. The platform is suited for teams that treat security compliance as a recurring operational program rather than a periodic audit exercise. ## How Organizations Approach These Options Although these platforms are grouped within the compliance or GRC category, they solve different problems. Enterprise GRC systems such as MetricStream, NAVEX, ServiceNow, and Diligent focus on governance, oversight, and structured risk frameworks. Security automation platforms, such as Vanta, Drata, and Hyperproof, focus on technical controls and certification readiness. Privacy and data governance platforms such as OneTrust address regulatory complexity in data handling. Process Street sits closer to the operational layer. It is designed for organizations that need to connect policies and controls directly to the work that staff carry out every day. Evidence is captured as tasks are completed, allowing firms to demonstrate compliance without requiring heavy manual preparation. Many organizations combine tools from these categories, allowing enterprise GRC to provide governance, security platforms to handle technical controls, and a Compliance Operations Platform to ensure that workflows are executed consistently, with proof generated in real-time. Related read in Business Process Management: Process Outsourcing Software.
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Building Cora: Our AI Compliance Agent
Vinay Patankar · 19 Jul, 2025 · Business · Document-management
When we started Process Street, our goal was simple. Help teams run recurring work without mistakes. The world didn’t need another task manager. It needed a system that could enforce standards, catch skipped steps, and give teams confidence that what should happen actually did. So we built it. A process management platform that made SOPs executable. A no-code workflow engine that turned policy into action. A tool that teams could actually use without calling IT. Over time, our customers pushed us further. Regulated industries brought their toughest workflows. Financial controls. Risk reviews. Policy certifications. Audit procedures. And that’s when it became clear. We weren’t just in the business of process. We were in the business of proof. From Process Management to Compliance Operations It wasn’t enough to help people document what to do. We needed to ensure it was done, every time, by everyone, with evidence. That’s where compliance operations come in. Compliance operations is what happens when you connect policies to workflows, workflows to monitoring, and monitoring to real-time action. It’s the difference between a checklist and a control system. Between paper compliance and actual enforcement. That’s where Process Street is today. Docs is where policies are created, governed, and versioned. Ops is where those policies become workflows, executed with full audit trails. And now, we’re building the intelligence layer to tie it all together. Enter Cora. Our AI Compliance Agent. Cora (which stands for "Compliance Orchestration and Risk Agent) is not a chatbot. Cora is not another assistant with a cute name. Cora is a system of enforcement. It watches how work gets done. Flags when it drifts from policy. Suggests updates when regulations shift. And generates the proof teams need to pass any audit, without the scramble. It’s not here to make compliance easier. It’s here to make it automatic. Why We’re Building Cora on AWS You don’t build a compliance-grade AI system on weekend infrastructure. Cora runs long sessions. Monitors real workflows. Triggers real consequences. We needed scale, security, and performance without compromise. That’s why we’re building on AWS. AWS AgentCore gives us exactly what we need. - Long-running agents that can observe and act in real time - Secure, isolated sessions that respect data boundaries - Deep integration into the services that power enterprise operations This is not a prototype. This is the foundation for the next generation of compliance enforcement. What’s Next We’re starting with the high-stakes use cases, capital markets, risk teams, and audit-heavy ops. But Cora is not a one-off. It's a system. A platform. A new layer for how compliance gets done across every industry. Process Street is now a Compliance Operations Platform. Cora is our intelligence layer. AWS is our partner in making it real. If you’re building the future of AI, compliance, or operations, let’s talk. No more missed steps. No more compliance theater. Just policy, executed. Related read in Business: Process Diagrams.
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