Airtable Launches Superagent and Enhances Visibility in Agent Executions

Airtable introduces Superagent as a new solution to improve visibility in the execution of multiple research agents. Launched on Tuesday, Superagent utilizes teams of specialized AI agents operating in parallel to perform research tasks.

The main differentiator of Superagent is how its orchestrator maintains contextual continuity during execution. Compared to previous systems that applied simple model filtering, the orchestrator of Superagent has complete visibility over the entire execution pathway, including the initial plan, execution steps, and results from sub-agents. This allows what co-founder Howie Liu describes as "a coherent journey" where all decisions are orchestrated.

"The essence lies in how you leverage the model's self-reflection capability," Liu told VentureBeat. The startup was founded over twelve years ago, initially as a cloud-based relational database.

Airtable has built its customer base across more than 500 thousand organizations, with 80% of Fortune 100 companies using its platform to create custom applications tailored to their workflows.

From Structured Data to Open-Ended Agents

Liu presents Airtable and Superagent as complementary solutions that serve different corporate needs. Airtable provides the structured foundation, while Superagent handles unstructured research tasks.

"We started with a layer of data. The name Airtable suggests this: it's a data table," Liu explained. The platform has evolved with automation features and scalable interfaces for thousands of users.

"Superagent is a very complementary form factor, which is quite open-ended," said Liu, highlighting that the decision to build these capabilities is based on market learnings regarding the use of increasingly advanced models.

How the Superagent's Multi-Agent System Works

When a user submits a query, the orchestrator creates a visible plan that breaks down the complex research into parallel workflows. For instance, when researching a company for investment, the task is divided into specific parts, such as analyzing the team and the funding history. Each part is assigned to a specialized agent.

Although Superagent is labeled as a multi-agent system, it relies on a central orchestrator that plans, dispatches, and monitors subtasks, ensuring a more controlled model compared to fully autonomous agents.

The Airtable orchestrator ensures total visibility over the execution process, avoiding contamination of the primary context while aggregating clean results.

The Impact of Data Semantics on Agent Performance

Liu argues that agent performance relies more on the quality of data structure than on model choice or prompt engineering. Based on an internal data analysis tool, the Airtable team demonstrates that data preparation takes more effort than configuring the agent.

"The hardest part to get right was the semantics of the data," Liu stated. The data preparation work focused on three main areas: restructuring data, clarifying representations, and ensuring agents can use them reliably.

Considerations for Enterprises

Organizations evaluating multi-agent systems must consider essential technical priorities, as indicated by Liu’s experience.

The data architecture precedes agent implementation. Internal experiences have shown that data preparation can consume more resources than agent configuration.

Context management is critical. It is essential to have an appropriate orchestrator that maintains state and information throughout the workflow.

Relational databases are important. Relational architecture offers cleaner semantics for agents' navigation than unstructured repositories.

Orchestration requires planning capabilities. Planned agendas are necessary to optimize results in interactions with agents.

"The fundamental proposition is that much relies on having an effective orchestration layer," Liu concluded, emphasizing the importance of maximizing the strengths of the models being used.