"We don't get hired for the floor" says chairman of Kirkland & Ellis, investing $500M building a custom legal AI. So I went down the rabbit hole on who else builds custom large scale automation for past many decades: Car factories across the world. There are handful of robots manufacturers (instead of 100s). But... Car design is a competitive advantage means every factory will have unique process. Car factory is a layered architecture. No one vendor can address all layers. Multi vendor/supplier is a deliberate strategy to avoid lock-in and process protection. Regulatory ensures there are local first strategy across the supply chain. Loved this note on Incumbents and it is a very well articulated thesis but I'm still on the fence on bunch of assumptions. Horizontal vs vertical, build va buy, service vs consult. Everything is still up for grabs. it's just too early!

🎉 Super excited to share our labor of love! 75 pages of it 😍 95% of GenAI pilots never reach production. AI models are not the problem. The gap between pilot and production is the process. The processes we had to build traditional software or buy SaaS are now obsolete. 🏁 Announcing our new e-book 🏎️ "The AI Discipline Playbook: Five Steps to Governed AI" We need a new systems thinking on how we build and operate AI keeping in mind costs, outcomes & risks. We wrote this book to set out the discipline that lays the foundation for new systems and process for Agentic AI. It reimagines a new way of buying and building with AI technology. It is not a product pitch. The discipline works regardless of who builds it. Five steps each one removing a specific cost or risk from your business. Five steps combined delivers a method to drive Agentic AI outcomes. When writing this book, our team has ensured we contrast our decades of experience with our accelerated learning in the AI era. But I will be direct. We built PitCrew because we believed this discipline needed a machine behind it, not just a book about it. If you want to see what it looks like running on real accounts, the last chapter tells you how. Free access: gopitcrew.com/five-steps @GoPitCrew @TweetSamG @rameshmay @arnavgoel_

No one likes to say it, "we ship our org chart." But it's true! If you truly understand the nature of how you build & what you ship, your culture is your product. Here is what we understand deeply at @GoPitCrew : AI Agents are 2 hops from the end user! Agents we use: Internal harness X Claude Code Us: product managers & engineers who read traces and recover "No, retry with X" and it recovers Human in the loop absorbs every error Reliability bar: 1X Agents our Agents build: PitCrew harness in buyers' hands Builders with no view into our harness internals Our errors silently become their defaults Our assumptions looks like a bug in their agent Reliability bar: 10X Agents our Agents ship Service bots, workflow agents End users who leave on the first failure No fallback, no patience, no debugging Our gap arrives amplified for every customer Reliability bar: 100X Everything we do at work everyday is keeping in mind those hops and its impact on product reliability. We built a culture & org around this understanding of agents. And that's what we ship! #Harness #Agents #EnterpriseSoftware

Saas was fun! @TweetSamG , @rameshmay and I have spent over a decade building and scaling software led businesses. We operated in a broad software category called System of Record (SoR), a central repository designed to store business data (customer info, employee records, or vendor contracts). We designed user experiences over these system of record which were opinionated (this is the only way you access customer data or this is right way to collaborate on a contract). These designs forced job functions like Sales or Marketing or People Ops to revolve around the silos of CRM or ERP or HRIS. For us the UX was the product. We created stickiness by enforcing data hygiene, promoting shared vocabulary and training muscle-memory around the usage of the product. We always assumed that the glue that connected these silos of systems of record is the human element. We saw first hand that organizational structures being built or evolve specifically to manage the boundaries of these static systems. (we are not proud of the frictions at these boundaries!) What made building business software hard was the maintaining of the workflows and scaling of the data model. These got harder over time as we had to solve for collaboration-based edge cases in growing organizations. New team or experts would bring in new requirements for approvals, governance, interactions, or newer systems of records. If we were to pin point one single thing which broke the camels back of business software: it was building and maintaining workflows. That changes now with AI Agents. Based on the battle scars we have building business software we believe AI agents will dismantle the need for human gatekeeping (“collaboration”)  between systems of record. AI agents act as navigators that transcend silos, guided not by rigid data model or opinionated workflows but by intent, goals, and guardrails. Unlike traditional software, AI agents stitch together context on the fly, accessing the specific records they need across multiple systems, and apply language model reasoning to deliver an outcome. This shift makes the legacy boundaries of SoR redundant (strong opinions in workflows don’t matter) as the agent focuses on the completion of the objective rather than the management of the record. Intent is to the system of work what data model is to the system of record. If the two key pillars of business software were: Data model and Workflows then the key pillars of System of work are: Intent, Goals and Guardrails. We at @GoPitCrew have designed and build a harness platform that can be configured to suite the goals environment, we enabled a intent capture system that guides the user to define the intent and guardrails. We have agent execution environment which complies to the guardrails and delivers measurable outputs. The outputs are then verified against specified goals. The idea of intent is not new, it’s like specifications or SoP’s. But communicating Intent for agents is not intuitive, in fact large models make it hard (perhaps by design to drive excessive token usage). Agents need to know the jobs to be done but also need to be made aware of the guardrails. Unlike human work, agents could claim goals achieved as judged by AI models, but the as judged by humans it could fall short, as judged by governance lens they can definitely mess up. The goals measuring systems need rubrics, evals and loops which ensure the goal is really achieved. Goal systems also ensures Agents operate within guardrails configured through governance policies, training loops, IP boundaries, compliance demands. Looking back at the systems we’ve spent the last decade building, we see the limitations of the boundaries we once defended. Looking forward, we see the potential for something far more dynamic. As builders, our role is evolving: we are no longer just the architects of data, but the stewards of intent. It is time to leave the frictions of static systems behind and embrace a future where our software is as adaptable and intelligent as the people it serves.

This is very first-principles practitioner notes. Thank you!

The Tokens of Playfulness: This the culture building exercise and the there is no way out of it than "Tokenmaxxing" No doubt tokens are being wasted in some of the experiments but then are no easy ways to guardrail and enforce token budget while adopting a new learning curve at scale.

This is very first-principles practitioner notes. Thank you! "system of verifier" is perhaps a key piece to enable system of action and system of coordination. In our own thesis we have categorised the knowledge base or SoR into 3 buckets: AI-native (cli, headless), AI-friendly (api, openness to access), and Anti-AI (no schema, no access, FTP servers) The convergence layer to these 3 buckets is: Verification i.e. How do we know an agent completed the task at hand correctly? This could jumpstart the entire work of reverse engineering workflows into automations to autonomous agents. Instead you invest in Intent capture and outcome verifiers. Experts, trusted technical partners, or "Forward deployed Experts" are perhaps the best option for buyers to start investing in verification infra. PS: The pace at which every piece in the AI stack is moving there are no technical buyer. There are just "wait and watch"-ers.

We are just getting started in a long journey of rebuilding the entire tool chain.

Builders could go from idea to working application in hours instead of weeks. The framework handles the infrastructure; humans handles the interesting problems. Agent harness are ready for the same intervention. What do most agentic systems need? @TweetSamG @rameshmay

At @GoPitCrew we visualise a workflow as a Kanban. Each task or swimlane can a subagent (wrapped in an eval).

Thank you for the partnership @googlecloud

Happy first anniversary to MCP 🎉 team PitCrew's policy driven grounding service right inside Anthropic's Claude. - Run a FINRA check on Social media post inside Claude. - PitCrew Ground's Claude's responses (and reasoning) @MCP_Community @TweetSamG @rameshmay