TL;DR — Edition #4 zeroes-in on three tectonic shifts for AI founders:
- Enterprise Agentics – agent frameworks such as Google’s new ADK, CrewAI and AutoGen are finally hardened for production, and AWS just shipped a reference pattern for an enterprise-grade text-to-SQL agent; add DB-Explore + Dynamic-Tool-Selection and you get a realistic playbook for querying 100-table warehouses with confidence.
- Data-as-a-Service (DaaS) – buyers no longer want “yet-another platform.” They want AI answers piped straight into the platform they already use, whether that’s an ERP, TMS, PDF or Word doc. Everstream and CaseMark show how API and MCP distribution is beating pure SaaS.
- Self-Improving LLMs – When it comes to the evolution of LLM post training, RLHF lit the fuse with the original ChatGPT 3.5, LoRA made fine-tuning cheap, DeepSeek proved RL + synthetic data can bake reasoning into weights, and Predibase just productized reinforcement fine-tuning for everyone. Next up: self-calibrating, self-improving loops that keep models fresh after deployment.
Enterprise Agentics
From demo bots to production AI
2024 gave us a flood of “toy” chatbots; 2025 is about enterprise-grade. There is a whole zoo of agent frameworks on the market, but Microsoft’s AutoGen, CrewAI, LangGraph and Semantic Kernel have all crossed the 1.0 boundary, each adding CI-style test harnesses and retry logic for real uptime. Google’s open-source Agent Development Kit (ADK) puts multi-agent design patterns (task graphs, tool schemas, A2A protocol) behind a Vertex-hosted control plane that ships with logging and policy guardrails.
There has also been a blossoming of tools made available to LLMs via MCP. If the number of agent frameworks is a zoo, the number of MCP servers is a rainforest. This is an opportunity but also a challenge: accuracy on reasoning tasks drops from 43% to 2% when a single agent is handed 50+ tools without curation.
AWS pattern: enterprise-grade text-to-SQL
AWS and Cisco just published a reference stack that balances accuracy, latency and cost for text-to-SQL agents in production—Bedrock for generation, Athena for execution, and a feedback loop that chooses lighter models unless confidence is low. It is effectively a blueprint for taking an agent out of the lab and into regulated enterprise data.
Automating database EDA for fine-tuning
The DB-Explore paper shows how to crawl a relational schema, build a graph, and auto-synthesize thousands of instructions to fine-tune the agent, lifting SPIDER exec accuracy to 84% with a 7 B-parameter model. That same exploration pass can emit tool definitions (one per table or join view). In a 500-table warehouse that means 500 potential tools—too many for a single prompt.
Dynamic Tool Selection meets MCP
Researchers now pair tool RAG (vector-search over tool docs) with frameworks such as Graph RAG-Tool Fusion and Toolshed KBs to pick the minimal tool set at run-time. The Model Context Protocol (MCP) formalizes those tool descriptors so any agent (OpenAI Functions, Google A2A, etc.) can load them lazily. Together, enterprise teams can query hundreds of tables without blowing the context window or vendor bill.
Data-as-a-Service
Why founders are pivoting from SaaS to DaaS
VCs report “platform fatigue”: users don’t want another platform; they want answers inside the tools they already use. That is pushing AI startups toward Data/API products that slot into existing workflows, often sold usage-based rather than seat-based.
Supply-chain risk as an API (Everstream)
Everstream Analytics exposes its real-time disruption scores directly into SAP, Oracle TMS and other ERPs. This enables supply chain managers to synthesis risk data with operational data to generate insights and automate workflows, all while maintaining data privacy and security. APIs and MCP make these integrations seamless.
Everstream still has a SaaS platform for users to do deep research into their supply chain risks. That’s where an enterprise-grade chatbot will shine. But the DaaS offerings enable Everstream’s data to also be integrated into enterprise-grade agents hosted on external platforms, where multiple data sources can be synthesized and operational workflows can be automated.
Legal-tech startup CaseMark bakes AI into Word and PDF: a one-click “Summarize deposition” button renders a narrative and page-line digest, then saves it straight back to the file.
Partners like Smokeball also distribute CaseMark’s AI insights to tens of thousands of lawyers, proving that embedded AI beats new UI for adoption.
Takeaway for founders
If your GTM hinges on dragging users to a new portal, expect challenges. Selling answers via MCP, API, webhooks, or document plugins makes AI feel invisible—and unlocks channel partnerships and integration budgets rather than software line-items.
Post-Training Evolution
Stage 1 — RLHF (ChatGPT moment)
OpenAI’s 2022 InstructGPT paper showed that Reinforcement Learning from Human Feedback (RLHF) could align a 1.3 B model better than a vanilla 175 B model. This was one of the main innovations that turned GPT 3.5, aka ChatGPT, into a usable product.
RLHF is effective but expensive due to the requirement to have a human in the loop.
Stage 2 — Supervised fine-tuning at the edge (LoRA)
LoRA (Low-Rank Adaptation) enabled fine-tuning of LLMs by tuning a small fraction of the number of parameters as the full LLM, letting developers customize open weights cheaply.
The drawbacks are that 1) you need labeled data to train on and 2) you can only train for narrow tasks (e.g. classification) vs general intelligence improvements.
Stage 3 — Reinforcement Fine-tuning with synthetic data
Chinese lab DeepSeek trained R1 purely with RL on model-generated trajectories plus the GRPO algorithm, hitting GPT-4-level reasoning at a fraction of the cost.
Predibase just turned this recipe into a managed service: drag-and-drop reward functions, curriculum schedules, and LoRA adapters under one hood.
Stage 4 — Self-calibrating / self-improving loops
Fresh research shows models that iteratively calibrate themselves during continual learning can slash expected calibration error after each round, producing more reliable confidence scores without new labels. Combine that with synthetic-RL and you get an always-evolving model that improves in-flight—no human in the loop except to define rubrics.
Closing Thoughts
The through-line this month is maturity: agents moving from hackathons to secure production stacks; AI products delivering data not interfaces; and post-training techniques that let models grow up after deployment. For builders, the playbook is clear: orchestrate > monolith, embed > platform, evolve > stagnate.
Thanks for reading! See you next week for the next edition of Idea Frontier.
