From the field.
What Jigar learns building and training, shared as posts. Specifics over slogans.
Tool registry design for agentic AI: how the wrong registry kills accuracy before the prompt is read
I reviewed a system last month with 47 tools in its registry and a 22 percent wrong-tool-selection rate. The team was about to migrate from Sonnet to Opus to fix it. The prompt was fine. The registry was the bug. This is the audit pattern I run on every client codebase before we change anything else, the seven failure modes I see in production, and the numbers from the cleanup.
AI agent vs agentic AI: what the distinction actually means when you ship one
Vendors blur the line because "agentic" sells. The two terms describe different architectures, with different cost shapes, different observability needs, and different scoping conversations. Here is the framing I use with clients and the three-question test for which one your project actually needs.
Gemini 3.5 Flash vs Sonnet 4.6: should you re-route your agent stack?
Google shipped 3.5 Flash this week with a "frontier intelligence plus action" pitch and a 4x output-tokens-per-second claim. If your routing layer is on Sonnet 4.6 today, this is the week to re-benchmark. Here is what I am actually moving, what I am leaving alone, and the cost-per-completed-task maths nobody is doing in public.
MCP governance just became a product: what Databricks Unity AI Gateway changes for enterprise agents
Every enterprise MCP deployment I have audited in the last six months has been hand-rolling tool-access policy, payload logging, and per-team cost limits on top of a gateway someone wrote in two days. Databricks just shipped that as a product. Here is what it actually changes, where the gaps still are, and the migration I would run for a Databricks shop.
Three paradigms of LLM memory: implicit, explicit, and agentic
A new survey from BigAI-NLCO splits LLM memory into three layers. Most production agents I review have built the middle one, called it memory, and skipped the layer on top. Here is what the taxonomy actually buys you.
Tool descriptions are prompts. Fix the registry, not the agent.
When an agent picks the wrong tool, the registry is broken, not the agent. Three rules I now apply before debugging anything in a multi-tool system: precise names, "when to use" triggers, and a curated load list. Anthropic's new tool-selection telemetry finally puts numbers on what changes accuracy.
The cheapest LLM call is the one you do not make. GitHub's 19-62% token cut, decoded
GitHub published an instrumented analysis of their agentic CI workflows and reported 19-62% token-cost reductions. The savings are the headline. The technique (pre-agentic data fetching and tool-registry hygiene) is the story most teams will miss.
Claude Opus 4.7's 1M context: when to RAG and when to just stuff it
A million tokens reliably is real now, but it does not retire RAG. It changes the calculus. Cost, latency, recency, and the prompt-cache angle nobody is talking about.
MCP 1.0 is here. What changes for the servers you already wrote
The protocol stabilised. Most working servers will keep working. Three places the new spec actually requires changes (auth profile, server registry, streaming-response semantics) with diffs from a real migration.