This is very much a skill issue an not a capital intensive task and one that, I believe will eventually lead to far more advanced capabilities than today's frontier LLMs.

New on the Engineering Blog: The access and permissions we grant agents should evolve with their capabilities. In our own products, we set these parameters through sandboxing, which limits the scope of any potentially destructive actions. Read more: anthropic.com/engineering/ho…

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_

The most expensive mistake in enterprise AI right now: treating FDEs as your whole transformation plan. Forward deployed engineers (FDEs) are important for custom deployments, but they won’t fix the change management issue most enterprises are facing. It’s likely more the former that Anthropic and OpenAI will continue to prioritize (and hire into the thousands, who knows). Beyond performance and cost, it’s systems integration, ROI, and literal usefulness that drive revenue and stickiness. *However* External FDEs, in my opinion, will not make your company an AI-first company. You can have the sleekest multi-agent orchestrations and still have the majority of your employee base hating AI, avoiding AI, and distrusting leadership decisions on AI. And we already know this because we see this in traditional SaaS too: you can customize the heck out of your Salesforce deployment, but that doesn’t mean your sales team will improve their data hygiene or even attempt to change the way they track and grow with it. Buying a fancier car doesn’t mean you magically learn to drive better overnight. If you’re an enterprise exec and FDEs are sold as the immediate and sole solution to your company transformation woes, walk away. It’s the combination of tech *and* people enablement *and* process reinvention that compounds into actual business outcomes. Large complex enterprises will stall out if they only prioritize the first.

The Forward Deployed Engineer (FDE) market is absolutely exploding. 📈 Indeed data shows FDE job postings skyrocketed over 800% as companies rush to shift from "generating AI" to "operationalizing AI." AI labs (OpenAI, Anthropic, Google) are aggressively hiring talent that bridges the gap between deep AI fluency and client-facing communication, with top notch total comp. If you have technical depth and sharp communication skills, you are looking at the ultimate unfair career advantage in the new era.

One interesting trend over the past year is how quickly vibes complete the full circle. In just the last few months, we’ve done multiple round trips: - PMs went from “the role is dead” to “we still need PMs” to “PMs might be the most important function.” - Software engineering went from “AI will wipe out coding jobs” to “new grads can’t get hired” to “software hiring is booming again.” - SaaS itself went from “software is dying” to SaaSpocalypse to “actually, AI is software too. We are so back!”

New for financial services: ready-to-run Claude agent templates for building pitches, conducting valuation reviews, closing the books at month-end, and more. Install them as plugins in Cowork and Claude Code, or use our cookbooks to run them in production as Managed Agents.

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.

How do you give an AI agent a GitHub token without the agent actually seeing the token? 🔐 We’re launching outbound Workers for Sandboxes. Programmatically inject credentials, log egress, and enforce zero-trust policies at the network level—all transparently. #AgentsWeek cfl.re/4tfSt1G

Early versions of Mythos Preview often exhibited overeager and/or destructive actions—the model bulldozing through obstacles to complete a task in a way the user wouldn't want. We looked at what was going on inside the model during particularly concerning examples. (3/14)