Inkeep

I might be 2 years earlier here but I've been thinking about when AI Agents actually make sense vs. when they don't. Here's a framework: AI Agents win when A > h. Translation: When the rate of agent iteration (A) beats the rate of human iteration (h) on a problem. Most "agent" pitches miss this. They sell automation on predictable tasks—FAQ bots, simple ticket routing, basic if-this-then-that logic. Those are fine, but they're 5/10 problems. You don't need agents for that. You need workflows. I think AI Agents make sense for the 9s and 10s: high-uncertainty problems where you don't know the answer upfront. Example: A Head of AI at Fortune 500 needs to evaluate which tools fit a company that large and complex. That's not a FAQ-bot problem. It's a "surface answers from internal docs, vendor specs, and team feedback—grounded in reality, not generic hype" problem. An agent can test 100 approaches overnight. A human team can test maybe 3 per week. That's the difference. Or take technical writing. Docs are never "done." They evolve as your product does. An agent can map thousands of support tickets, identify gaps, draft updates, and adapt as feedback comes in—faster than any human could manually iterate. The pattern: Agents get you from "we don't know" to "here's the answer" in hours instead of weeks. They test, adapt, and learn faster than manual iteration ever could. But—and this matters—only if they're grounded in your product reality and you keep final say. Fast iteration within guardrails, not autonomous chaos at scale. The shift: Stop thinking "what tasks can I automate?" Start thinking "what problems require 100 iterations to get clarity?" That's where A > h. That's where agents belong. If your problem is a 9 or 10, agents beat humans every time. If it's a 5, save yourself the complexity. We're building for the 9s and 10s. Bring the tough problems—the ones where speed to clarity matters and uncertainty is high. That's where this gets interesting. PS: Written with support from Inkeep brand-guide agents.

With OpenAI launching AgentKit, our very own Gaurav Varma undertook a deep dive into the platform to comapare it with n8n. Key takeways: 1) AgentKit's is primarely built for workflows, not AI Agents 2) AgentKit excels at creating polished chat interfaces 3) So unike n8n, which has less developed chat UI, it struggles to provide autonomous agent routing and model 4) Inkeep combines the best of both: superior UI customization with truly autonomous agent routing We also we compare its architecture, developer experience, and UI capabilities against n8n and Inkeep to help developers choose the right framework. More here -> https://lnkd.in/dp5xu45n

With OpenAI introducing AgentKit, we felt compelled to help newcomers understand key concepts between 'Workflows', 'Agents' & 'Multi-Agents'. 1) Workflows follow **predetermined** paths -- meaning that they built before to get work done 2) Agents, on the other hand, are autonomous assisstants reason dynamically to decide how to get work done It's like the difference between a train (workflows) and a Waymo (agents). One runs on tracks, the other figures out the best route. OpenAI’s new AgentKit seems (for now) closer to the train category — structured workflows rather than fully autonomous agents. More here -> https://lnkd.in/g3yXKFVU

LLMs are increasingly having context anxiety. When Cognition AI rebuilt Devin, they discovered the model took shortcuts when it thought it was running out of context, even when there's plenty left. Full analysis -> https://lnkd.in/d9QdgmzC

Builders, assemble. The 'Financial Agents Hackathon' is happening tomorrow, Oct 3rd, at Microsoft in Mountain View. We're teaming up with LandingAI, Pathway, Devnovate, Lovable, APARAVI, Flexprice, and JUSPAY to host 200+ builders for an intense day of coding. The goal: ship a powerful financial AI agent in under 8 hours. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗹𝗶𝗻𝗲? - Over $10,000 in cash and prizes. - A chance to demo your winning project live on stage. 𝗬𝗼𝘂𝗿 𝗧𝗼𝗼𝗹𝗸𝗶𝘁: The Inkeep Agent SDK is the fastest way to ship a full-stack AI agent. Build with TypeScript or our no-code editor, visualize your workflow, and deploy instantly. 𝗙𝗶𝗻𝗮𝗹 𝗗𝗲𝘁𝗮𝗶𝗹𝘀: - When: Doors open at 8:30 AM tomorrow, Friday, Oct 3. - Where: Microsoft, Mountain View. Let's build the future. See you there! #HackwithBay #AIHackathon #FinTech #BayAreaTech #Microsoft #Pathway #LandingAI #Devnovate #Innovation #Developers

Context rot is the silent killer of AI agents. As context windows grow, model accuracy actually decreases. The shift from crafting perfect prompts to managing an LLM's 'attention budget' is changing how we build AI agents. Key insight: context is a finite resource with diminishing returns. More here -> https://lnkd.in/dn2i6xts

💡If your startup is not using Inkeep you should sign up today. It is a game changer. Two years ago Nixtla got early access to their offering and we are a very happy customer. 🤖 The Inkeep bot, has answered tens of thousands of questions from our open source users across different channels like web and slack. It has been extremely cool to see firsthand how Nick G. and team have relentlessly shipped new features at a massive speed. I’m even more excited for what comes next. And of course: the website is fire 🔥.

In this blog, we dive into when RAG beats fine-tuning, how to implement it, and real-world applications. Key Takeaways ➡️RAG retrieves relevant context at query time to ground LLM responses in facts ➡️Use RAG when you need fresh, factual answers; use fine-tuning for tone and domain-specific behavior ➡️ Most production systems use hybrid: RAG for grounding + fine-tuning for style ➡️ RAG costs come from indexing, retrieval, and generation—caching dramatically reduces both cost and latency ➡️ Long-context models don't replace retrieval; RAG still provides targeting, governance, and citations https://lnkd.in/dUjTGwsg