Ramesh May

Co-founder tech junkie, love sports, food and travel

[Rishi Kulkarni @rishikulkarni]

Great to see Anthropic kicking off harness ecosystem conversations.

[Ramesh May @rameshmay]

Stop by @GoPitCrew booth at #synergy26 to see PitCrew agents in action .. this is not a fancy demo, it is the real deal and solves your most time consuming tasks !

[Rishi Kulkarni @rishikulkarni]

Team @GoPitCrew is in Washington DC next week at #Synergy26 hosted by TradePMR

Connect directly with our team members for a hands-on deep dive into PitCrew's agentic platform for Financial services.

@TweetSamG @rameshmay @arnavgoel_

[Ramesh May @rameshmay]

@gregisenberg For comapnies with $25-$50M seed round valuations, what characteristics did you notice ? And presume they were all B2B ?

[Ramesh May @rameshmay]

@DavidSacks @Benioff True that .. but the massive layoffs by large tech companies is fueling the AI will kill jobs narrative. The current callouts of AI will increase jobs and no it will suck out jobs feel like a dog that is both barking and wagging its tail. Which end to trust ?

[Ramesh May @rameshmay]

@nathanclark_ Need agent to remember other agents

[Ramesh May @rameshmay]

@MilkRoadAI And hence the explosion of conversation around FDE and how your platform PLUS engineers/experts who has solid understanding of the domain, customer problem statements, with ability to deploy (rein-in) AI agents in that environment, becomes the “product” moat

[Ramesh May @rameshmay]

@levie Many financial services firms are also running systems that don't talk to each other. Before deploying agents, you need to solve data plumbing problems. Making agentic AI work against a firm's actual data and environment is the real effort. That's why the FDE model matters.

[Ramesh May @rameshmay]

@deedydas Understand the sentiment but doesn’t have to end up this negative. Maybe this is an oppty to up skill, find purpose, be even more relevant .. it is still early days in AI and not doomsday (yet). Although there is a lot of noise, mindful and motivated folks can cut through it

[Rishi Kulkarni @rishikulkarni]

FDE should be seen as Expert partners!
in an almost week by week upgrade to AI infra it's hard to catchup for businesses while focussing on their own KPI. Build a multiple stage partnership perhaps for stage 2 before you upgrade on own to stage 3

[Rishi Kulkarni @rishikulkarni]

There is more to it as you peel the layers of FDE:

- you get to work with customers directly. Understand their pains and processes.
- everyone is a product manager "on-site" with AI as a common language and UX.
- build muscle working with the governance teams (legal, security, finance). True career moat.

[Rishi Kulkarni @rishikulkarni]

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.

[Rishi Kulkarni @rishikulkarni]

SDLC as we know has been ripped apart. There are 100x new changes coming in but that they would happen in a short time seemed more of a click bait headline than wisdom.

💯👇 well said

[Rishi Kulkarni @rishikulkarni]

Excited to share a template of FDE plan which we are executing at @GoPitCrew

Everyone has their own definition of what the 'E' in FDE means.

[Rishi Kulkarni @rishikulkarni]

Intelligence paradox: Abundance in intelligence creates more trust debt.

"In both scenarios, users stay firmly in the loop—reviewing, iterating on, and approving Claude’s work before it goes to a client, gets filed, or is acted on."

[Ramesh May @rameshmay]

Appreciate you highlighting AI deployment problem . The question for enterprise leaders is how are they going to deal with the AI deployment chasm? Will they continue the layoffs or upskill them or get help from outside for agent deployment ? AI transformation oppty is huge

[Aaron Levie @levie]

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today.

The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do.

First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents.

Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do.

Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes.

Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design.

All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it.

This is a huge opportunity right now whether you’re doing this internally or as an external business provider.

[Ramesh May @rameshmay]

@heygurisingh I think it always comes down to basics - identify (aka understand) the problem, solve it, create outcome. And this applies to pre-AI, NOW (with AI) and the potentially "coming soon" AGI days as well.

[Ramesh May @rameshmay]

@_CallMeMacy Yes we vibe coded our CRM as well with just necessary functionality we really care about .. and that is good enough. Just need to be careful in going beyond strict MVP and having to deal with too many bugs/issues, etc.

[Rishi Kulkarni @rishikulkarni]

@levie We wrote this today "Vibe coding gets you 80% of the way there. Core logic. The remaining 20% — polish, testing, integrations, error recovery, audit trails, compliance is where real context engineering discipline lives. "

@TweetSamG @rameshmay

https://t.co/hAhQ64BuQR

[Ramesh May @rameshmay]

AI enables organizations and individuals to play offense through sustained augmentation and expansion.

That's what we're building at PitCrew. Our agent builder lets teams turn their own workflows into running agents, so the same people cover more ground.

[PitCrew Agents @GoPitCrew]

https://t.co/P2CFK6OBSW

[Nikesh Arora @nikesharora]

I would have expected the market to start discerning between SaaS that is impacted by AI, SaaS that needs to evolve, and SaaS that benefits from AI. Analytical SaaS, Creative SaaS is in category 1, System or Record, Human workflow and Engagement and Productivity are in category 2 and Infrastructure SaaS and Cybersecurity are in 3. This constant paranoid reaction of the market will continue to create buying opportunities for the discerning.