Nexla Inc retweeted
24 hours till registrations close.
The workshop lineup for Production Agent Lab is here.
Join us October 22 in SF for three hands-on sessions with Amaury Desrosiers (@nimble_search) Abby Morgan (Opik by @Cometml) and Abhijit Bharadwaj (@NexlaInc)
Grab your spot: luma.com/agentslab
Most "context layer" definitions describe metadata about your data. An inventory with better search.
@NexlaInc we know context belongs in the data itself, applied on every request an agent makes, not looked up as a separate step.
Here's the distinction: nexla.com/what-is-a-context-…
The @G2dotcom Fall 2026 results are in, and it's our customers behind every number.
29 reports, 18 new. 9 badges.
Momentum Leader in Active Metadata Management & Event Stream Processing.
Thanks to our customers who shared a review and our @NexlaInc team for listening to them. 🙏
#G2Fall2026
Everyone's talking about context for AI agents. Almost no one's talking about the tool schema, the one file a model reads on every single call.
Vague schema: 708,973 tokens, 18 tool calls to answer a question. Grounded schema: 21,682 tokens, 6 tool calls. Same answer.
This series on Context from team @NexlaInc is an attempt to highlight the various aspects organizations have to account for beyond the buzz word.
nexla.com/blog/mcp-tool-sche…
#MCP #AIAgents #Nexla
Most "best no-code ETL tools" lists rank by connector count and call it a day. That misses the part that actually matters: what happens when a source's API changes, and whether transformation requires SQL underneath the "no-code" label.
We compared 8 tools (Nexla, Fivetran, Airbyte, Stitch, Matillion, Skyvia, Domo, Hevo) on three axes: connector depth, transform completeness, monitoring.
Fivetran and Airbyte are no-code for extraction and loading, not transformation. Fivetran's own site calls its connectors "pre-built ELT pipelines," deferring the "T" to dbt, which needs SQL. Worth knowing before you assume "no-code" covers the whole pipeline.
The category is also shifting constantly: Matillion rebranded its whole platform to Maia in early 2026, repositioning as an AI data automation platform. Domo's AI and data business is being acquired by Progress Software, deal announced July 22, 2026.
#ETL #DataEngineering #NoCode
Loop engineering: AI systems that discover work, orchestrate agents, run autonomously.
The reliability problem is usually data. Bad connectors, stale schemas, missing context break loops before orchestration can help.
Nexla is the data layer under the loop. 900+ connectors, task-specific, governed data built for AI and agents.
Partnering with @tokensandai @cursor_ai @AirbyteHQ July 17 SF.
Register Now! luma.com/loophack
One finding from the 2026 State of AI Agents report from #Databricks - companies using AI governance tools get 12x more AI projects into production. Governance is usually framed as a constraint. The data says otherwise.
databricks.com/blog/enterpri…
#AgenticAI #DataGovernance
Projects without governance tooling die answering the same questions post-build: Who owns this data? What can the agent access? Is there an audit log?
Governance is not a gate at the end of the process. It is the foundational infrastructure before you build the process.
At @NexlaInc we are focused on governed task-specific data for your agents, so you can answer the foundational questions before your agents go into production. nexla.com/mcp
Most AI agents don't fail on compute. They fail on missing context.
@NexlaInc CEO @saketsaurabh, joined @theCUBE's @furrier in this AWS Marketplace Series to talk through our Agentic Integration Platform that turns prompts into data pipelines and MCP servers, that can connect over 700 systems from CRMs, finance, support systems and provide the agents with the task specific governed data to complete their tasks.
Context, not compute, is the real engine of agentic AI.
Watch full interview: youtube.com/watch?v=vJG0V-7H…
Nexla Inc retweeted
RevOps team I spoke with last week "renewal agent flagged wrong deals, ran slow, burned 3x tokens".
Root cause: vendor MCPs with no shared context. Salesforce. Snowflake. ERP.
Task-specific, governed MCP servers fix this for agents
#MCPServers #GTMOps #RevOps
2 weeks since we launched MCP Studio from @NexlaInc and we have been testing and benchmarking.
Same BigQuery task through two MCP servers. Same model, same warehouse, only the server changed.
Generic BQ MCP: 12 tool calls, 7 failed, 112K tokens, 50s
Nexla MCP: 2 tool calls, 0 failed, 39K tokens, 17s
The difference: system MCP vs task specific MCP.
Full benchmark breakdown in comments.
#MCP #AIAgents #DataEngineering #EnterpriseAI
The #1 reason AI agents fail in production isn't hallucination. It's data access. Plumbing problems don't show up in benchmarks. They show up in production.
What data access problem have you run into with AI agents in production?
#DataForAgents #AIAgents
Every enterprise is using the same LLMs. The AI moat in 2026 is the data. Your agents are only as good as the data they can reach. What is the data access gap that is limiting your AI deployments right now?
#EnterpriseAI #DataForAgents #AIStrategy
Enterprise AI agents fail in production for one predictable reason: the data isn’t ready.
Score your data platform in 60 seconds with our Agent Readiness Scorer, built on the 7 pillar framework, plus a 3-year cost calculator: hubs.li/Q04jX9mG0
#DataForAI #AIAgents
