Agent Interface for Social Data: The CTO Edition
By Tyler Logtenberg
July 2025 | 20 min. read
Table of Contents
“LLMs can’t reason with the real world if you can’t pipe the real world in.”
The broken patterns between social data access, and agentic products.
Underneath the layer of marketing language where everything is “agents” and “AI”, is a missing element. The technical and product leaders of products consuming social data know the data layer is still duct-taped together.
If you are a CTO, product leader, or engineering leading; then you likely know what I mean.
- Agents are getting smarter, and customers are expecting their products to answer questions with less steps, and with human language.
- Scrapers, and data vendors have fragmented, rate-limited, and in-flexible APIs
- Agents frameworks aren’t designed for collection and enrichment, limiting to underlying LLM model access to search results and select networks like Reddit.
Powerful features leveraging LangChain, RAG, agentic workflows, amazing prompt chains… all struggling to connect to real-world data across social news, UGC, etc.
The gap is due to multiple breaks in the interactions between the data and systems
The gaps between plugging together the two sides is based on a number of elements. The following are the four biggest gaps that kill 99% of engineering implementations involving social data.
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Data requests are brittle
Many data scraping technologies require specific query language, patterns, data/time formatting. Some of the day-to-day breaks include timezone handling, lucene vs boolean vs others, specific request sizes, and many endpoints for a single service.
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Data results lack standardized structure
Even across premium data aggregators, the schemas do not follow any common patterns. Every source has their own schema patterns, and even unique application of schemas between sources at a single vendor. While this is a massive break on its own, the data often also varies in file format, delivery method, and collation.
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Data providers are built for larger queries
1st party social data, and even 3rd party data providers are built on a model of queries of returning multiple results for every initiated search parameter. Some of these providers deliver data instantly, others provide an ID to check in later, and yet others are built to write the data into file stores. Asking cohesive questions across multiple sources with minimal latency, and few results per query, is often a breaking pattern for many.
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Agent frameworks are not designed for dissection of human language into social data
When passing a prompt to an LLM, the dissection of the ask into engaging a RAG framework is often taken for granted. Leveraging internal data or the underlying LLM’s own framework is an easy solution. When it comes to social data, the data is not pre-existing or easy for reference. This requires a conversion of the request into multiple (source-specific, non-standardized, and brittle) data requests, with an expectation of structured and enriched data response with minimal latency.
Bridging the gap: A unified interface for social data
Datastreamer’s platform has been unifying and powering data pipelines for platforms using social data, and doing so at high scale. With 90% of the common problems being already solved, the final piece of the bridge laid in agent-to-agent protocols.
The platform already offered a number of the key elements required to fill each gap.
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Schema-agnostic performance available through Unify and Dynamic Pipelines
The existing technology within Dynamic Pipelines already handled converting various incoming data schemas and formats into the same patterns using Unify. In addition, filtering, routing, and enrichments are applied to bring standardization to the metadata itself. Ensuring all content, regardless of the data source or status, was brought to a normalized level. Solving the issue of data lacking standardization in results.
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Job system, components, and Job automation to handle data ingestion and processing
With hundreds of integrations already built and managed for working with many data providers and sources, the brittleness and changes present within the sources of data themselves is reduced for pipeline users today. Bringing it down to a whole new level, customers using Datastreamer to power their pipelines, leverage the Jobs system. Acting as a layer between the data source itself and the requests, the Jobs system handles the requests, health monitoring, data volume controlling, error handling, and async behaviours of multiple providers at once. Also bringing further automation into the process by tracing and tagging each piece of data with originating request. Solving not just the issue of the brittleness of data requests, but also providing the additional layer of handling countless requests and sources at the same time. Bringing a solution to the patterns of data providers not being agent-ready.
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Datastreamer’s own agents and Orchestrator to tie it together
Orchestrator has been faithfully handling the high-demands for pipeline flexibility, new customer features taking off in usage, and the (developer adored) ability to blend modular pipeline building with instant deployment. To bring the last 10% needed for Datastreamer to be the agent interface for social data, came the release of Datastreamer’s own agents.
These agents are working alongside Orchestrator, and every other capability mentioned to solve the fourth and biggest issue. Agents aren’t designed for the dissection of natural language request to the complex world of social data.
The agents that Datastreamer have launched are able to handle that last missing piece, taking natural language prompts, and with knowledge of the capabilities of your data pipelines in mind, is able to convert and execute into on-demand data retrieval.
"Datastreamer is the interface layer between autonomous agents and the dynamic, high-signal world of social data."
Real world example of agentic flow with social data
“What is the sentiment on social media towards (competitor)’s big launch yesterday?”
A user in your platform asks your new feature that prompt, and they are hoping for powerful insights to drive their decisions. So what is happening behind the scenes?
You can easily create an agent to be able to deliver those insights to your customers, if the right data is accessible.
Depending on roadmap execution, there are a few scenarios:
Having the data ready (current best-practice)
The current best-practice (but pre-agent interface) approach would be to already have the data ready. This would be done with automated data collection, running in rapid periodic intervals (intra day) and executing pre-built queries for the keywords, terms, and sources required.
As data returns then the encoding, normalizing, enriching, and structuring are all firing in your pipelines. Delivering it to the a RAG-ready data destination. If you are already doing this, then you are likely using Datastreamer, or are a glutton for punishment and built it over the past years.
However, there is still a risk. Data gaps will appear as adoption increases, and customers will expect results for their prompts which would not resolve. Those prompts from customers would likely require data that you have not pre-collected.
Relying on LLM and internal underlying datasets.
If you do not have the data ready in a RAG-ready manner, then answering the customer’s prompt to your agent would take a different path. You AI features would need to rely on existing knowledge from the underlying LLMs, manual collections, or sampled data. As tolerance for AI gaps is very low in consumers, this would be a very short-lived feature.
Agent Interface approach
The agent interface scenario is the third, and makes the two previous seem heavy and expensive. It delivers rapid response, tailored, and without any non-required data.
- The end user asks “What is the sentiment on social media towards my competitor’s big launch yesterday”.
- Your agent, using it’s own scoring and/or confidence checking, realizes the data is not sufficient to generate the insights and prompt response.
- It then passes the request to the Datastreamer agent, running on top of one of you data pipelines.
- The Datastreamer agent, knowing the sources and capabilities it has access to, dissects the prompt into structured parts (like source types, timeframes, keywords, etc), and based on the social data sources present in the pipeline, is able to generate source-specific data requests.
- Using the technology we covered above (such as Jobs, Orchestration, and pipeline components, unification and structuring elements), data is retrieved, augmented, and delivered.
- Your agent receives the missing data, and can confidently respond with the most up-to-date and comprehensive insights leveraging every source that you worked with.
Product manager bonus: The data coming from the agent approach is heavily-geared to what it is your customers actually find valuable. Resulting data, as within a dynamic pipelines can take other pipeline paths to power aggregated features. Such as powering stronger historical search using other database technologies, or even being used to power micro-predictions and insights in other features of your product.
Forward-looking
From our perspectives at Datastreamer, we’re working to remove even more of these gaps. Our focus lays in other gaps around pipeline design, data procurement, and deeper agent-interfacing. If you are looking at your roadmap and deciding the next AI/agent features in your products, give us a shout.
