Datastreamer is Building the Agentic Interface for Social Data
By Tyler Logtenberg
July 2025 | 20 min. read
Table of Contents
Solving The Bottleneck Between AI and Social Data
Agents are changing software platforms, it is obvious. There however is a challenge that many of Datastreamer’s customers are looking to solve. Feeding these features effectively.
While Agents have taken off, the social data they consume has been left behind. This has created a large bottleneck and gap, stalling the growth of these capabilities.
But not anymore.
At Datastreamer, we’ve been quietly preparing for this moment. Since our creation, we’ve built a platform that powers the data pipelines of many of the world’s most innovative social listening, intelligence, market prediction, and insight platforms. We’ve solved data integration issues from the start.
Agents Can’t Act on What They Can’t Access
In roadmaps across every industry we support, there is a common thread. Agentic workflows are bringing the promise of great automation.
Within industries like social listening, many platforms are seeking to bring a human prompt from their end-users, and automate the generation of key industry insights.
“What is the sentiment towards our last product launch?”
If this social listening company wants to truly power an agentic workflow, they’re stuck with a hard truth: most of their data pipelines aren’t agent-ready. The agent can only operate on what’s already been collected, or what it can pull from generalized sources like search engine APIs.
Real-time ingestion? Still manual. Source diversity? Fragmented. Querying? Inconsistent.
At it’s core, this is a data pipeline problem. The right data is not able to be automatically collected, merged, enriched, and delivered; and the agents are therefore left with basic actions and value.
- If the customer of a competitive intelligence SaaS asks an agent for insight into a product strategy, it’s likely relying on pre-collected datasets, or shallow search results. Giving shallow insights.
- If the user of a brand monitoring tool wants to analyze shifts in sentiment, their insights are limited to what’s already pre-stored, not what’s happening right now.
- If the analyst at a threat intelligence platform wants to validate a threat, the platform’s agents rely on pre-collected data or limitations to specific sources, knee-capping their abilities to understand the validity of a threat.
The disconnect isn’t in the agents and the AI-powered features, it is actually inside the data workflow.
Many of today’s systems were built for analysts, not autonomous actors. As a result, the workflows remain limited, brittle, source-specific, and manual, when they should be fast, adaptive, comprehensive, and intelligent.
Agentic Interface: A Natural Evolution
For Datastreamer, this isn’t a new challenge for us, it’s a natural evolution of what we’ve already built.
We’ve spent years solving the exact problems that are now holding agentic systems back.
We’ve built the pipelines, the enrichment layers, the delivery logic, and the schema translation necessary to make messy, real-world data clean, fast, and usable at scale.
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We’ve already built the systems to manage, integrate and coordinate hundreds of disparate data sources that aren’t agent-ready.
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We’ve already solved the instability in social data. All of the: inconsistent query logic, mismatched schemas, variable delivery methods, and unpredictable update cycles.
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We’ve already been the leader in the offering to rapidly design data flows that are modular, scalable, and optimized for automation.
In short: we’ve been powering intelligent workflows before most people were calling them “agentic.”
Now we’re productizing that foundation, and making it available for all of our customers to power their own Agents.
Datastreamer is happy to introduce that we are the interface layer between autonomous agents and the dynamic, high-signal world of social data. And it’s not just a vision, we’ve already there.
"Datastreamer is the interface layer between autonomous agents and the dynamic, high-signal world of social data."
Introducing Agent-Powered Data Collection
The first stage we are offering is Agent-Powered Data Collection.
Using a lightweight agent-to-agent protocol, we’ve enabled your Agents to directly communicate with Datastreamer’s own Agents to receive prompts, construct jobs, trigger pipelines, and deliver enriched data all autonomously. Providing your platform with a direct interface to the world of social data.
How it works:
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Set up a pipeline, within Datastreamer, designed for agent use, adding your data sources, enrichment components, and rules. Choose how you want to enrich data, so it can be the most useful for your platform.
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Your platform’s Agents can then communicate with a natural language prompt or a structured request. Handling any number of data sources and size.
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Within those pipelines running on Datastreamer, Datastreamer’s Agents convert the requests into structured queries using each data sources limitations and rules. Triggering the retrieval.
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Returning data is then processed by Datastreamer’s proven Pipeline orchestrator to unify, enrich, and deliver the data back to your Agent and platform.
All automated, and without human intervention.
It’s the first step in a full agentic data lifecycle: from prompt → data retrieval → to action → to insight generation.
What does it enable for your platforms?
- Your platform can now automatically collect data when and how it is needed.
- Your Agents now have the ability to interact with any of your data sources in a customer-ready manner!
- Your engineers are freed from creating data pipeline workflows for various sources and their own rules.
- Your analysts are freed from converting customer asks into API calls.
Solving Real Problems for Modern Platforms
Being the Agentic Interface for Social Data, allows Datastreamer to provide platforms with a smarter way to build products that adapt, respond, and evolve in real time.
Here’s what this unlocks for companies on the front lines:
Competitive Intelligence gets Live Intelligence
Competitive Intelligence platforms can now offer agents that pull live insights from forums, review sites, and social media, not just pre-ingested datasets and internal CRM or report data. That means fresher insights, fewer blind spots, and faster reactions. Winning the competitive intelligence space.
Brand Monitoring gets Traction Awareness
Instead of relying on batch reports, per-source monitoring, or generic listening tools, Agents can now trigger on-demand cross-source data pulls when an anomaly is detected. Allowing their customers to rapidly understand, analyze, and monitor on their speed.
Market Research get Tailored Insights
Research platforms can now offer their customers with agents that collect data tailored to the customer’s specific question. No longer tied to Google Search results or what the underlying LLM may have access to.
Intelligence Platforms get Dynamic Sourcing
Your agents aren’t limited to what you’ve stored. With Datastreamer Agents + Datastreamer Pipelines, they can query, collect, and synthesize data from new sources dynamically. Giving Agentic workflows the freedom to use the open web like an extendable dataset.
These aren’t hypothetical scenarios. They’re what our customers are working to build right now.
Getting More Technical: RAG, Pipelines, and Agentic Orchestration
Under the hood, this shift unlocks a powerful architectural change for teams building with Retrieval-Augmented Generation (RAG), orchestrated agents, and hybrid pipelines.
Most RAG workflows today are only as good as the datasets they point to, and those datasets are often static, stale, or incomplete. Social data is especially difficult as every source and vendor use different standards, query language, performance, metadata, and more. Something that Datastreamer’s platform already handled.
Agents working in LLM stacks are incredibly smart, but still constrained by what they can retrieve. Datastreamer changes that.
With our pipeline-first architecture, developers can now:
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Save costs, and make data more meaningful by dynamically collect source data at query time, and not weeks before.
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Use natural language prompts to define jobs, reducing the need for manual pre-configuration, and ongoing educations on the minutiae of each sources and vendor.
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Feed custom RAG corpuses with targeted, high-signal data from social, news, video, and more; all within one spot.
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Move from pre-collection to using results directly by feeding into vector databases, custom tools, or LLMs. Either via webhook or API.
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Enable multi-agent orchestration workflows, blending internal agents, Datastreamer’s platform, and tools like LangChain, CrewAI, or open source frameworks.
- Use the proven high-speed and high-volume architecture, available natively within the Datastreamer platform.
These Agents from Datastreamer let you upgrade your data inputs so your stack can actually think. And, with versioned jobs, clean JSON, and flexible enrichment layers, Datastreamer becomes a source of truth for AI workflows that need more than just search results and static snapshots.
The Road Ahead: How We Are Expanding the Interface
We’re not stopping at collection. The future of agentic data requires deeper integration, smarter automation, and flexible pricing that mirrors how agents actually work. Here’s what’s coming next:
Stage 1: Agent-Powered Data Collection
Agents can trigger collections, run jobs, and receive enriched results via webhook. All powered by Datastreamer pipelines.
Stage 2: Agent-Powered Data, Anywhere.
No agents? No problem! Expanding our own Agents to handle creation of new data jobs across Portal and APIs, using nothing but a prompt; complete with source selection, query generation, and more. Allowing you to be Agent-powered without a line of code.
Stage 3: Agent-Workflow Pricing Support
We’re aligning pricing with agent workflows by introducing a flexible, credit-based model. Every run, collection, or enrichment will be metered, and transparent. Allowing you to adapt your own internal costs to your AI feature pricing models.
Stage 4: Agent-Led Optimization
Datastreamer’s Pipeline Orchestrator is already self-healing, scaling, and automated in it’s support of your workflows. We will be bringing it to the next level. Rolling out additional intelligence on data and enrichment gaps that your own customers are looking for when they use your product.
Stage 5: Changing the Paradigm: Automated Pipeline Creation and Sourcing
What if you could offer your customers complete freedom? Our vision is to roll out the capability to allow your customers to define what they need in plain language. Following your rulesets, Datastreamer will generate the pipeline to solve the need, even sourcing the data vendors and enrichments required to answer the customer’s data requirements.
Unlocking the Full Power of Datastreamer
When we started Datastreamer, we weren’t chasing trends, we were solving a hard problem we knew would matter more over time: how to make messy, unstructured external data useful.
Now, with the rise of agentic systems, everything we’ve built suddenly clicks into place. This isn’t a pivot. It’s an unlock.
If you’re building agents that actually need to see the world, not just hallucinate answers from outdated indexes.
Let’s make AI more than just clever. Let’s make them ready.
