Our Agent Roadmap: What We've Built, What We're Building, and Why

The average enterprise data team of 20 engineers loses over $1.3M per year in engineering capacity to reactive maintenance — debugging pipelines, triaging alerts, processing access requests, coordinating schema migrations. That is before you count the $150K-$540K per hour cost of data pipeline downtime.

The problem is not that your team is not good enough. It is that the work follows the same patterns every time, and nobody has built the agents to handle it. Until now.

Why 11 Agents, Not 1

A single 'AI data agent' that tries to do everything will do nothing well. Data engineering has distinct domains — incident response, quality monitoring, schema evolution, governance, pipeline building — each with different failure modes, different tool integrations, and different trust requirements.

We designed 11 specialized agents, each purpose-built for one domain. Each is an MCP server that can operate independently or coordinate as a swarm. You start with the ones that solve your most painful problems and expand from there.

The Trust Ladder: Why Order Matters

We are not building all 11 agents at once. The order follows a trust ladder — we earn trust with low-risk, high-value agents before expanding to agents that touch production data.

• •Phase 1: Read-only agents. Incident Debugging and Quality Monitoring observe and report. They do not change anything in your environment. Low risk, high value. This is where we start.
• •Phase 2: Scoped actions. Schema Evolution and Pipeline Building can make changes, but scoped and reversible. Approval workflows. Dry-run modes. You control the blast radius.
• •Phase 3: Sensitive domains. Governance, cost optimization, and migration touch production data and access controls. These come after we have earned trust with lower-risk agents.
• •Phase 4: Meta-layer. Agent Observability — the agent that monitors the other agents — comes last because you need agents in production before you need to monitor them.

This is not arbitrary. Only 13% of enterprises plan to deploy AI agents in production (Gartner). The trust barrier is the number one obstacle, and you do not overcome it by shipping 11 agents on day one.

What Each Agent Solves

Here is what the 11 agents address and the maintenance tax each one eliminates:

• •Incident Debugging Agent — Resolves 60-70% of incidents without human intervention. MTTR drops from 4-8 hours to under 15 minutes.
• •Quality Monitoring Agent — Cuts alert noise from 50-100/day to 5-10 actionable alerts. Auto-remediates known quality issues.
• •Schema Evolution Agent — Prevents the 30% of incidents caused by schema drift. Detects changes, maps impact, generates migrations.
• •Data Context and Catalog Agent — The backbone. Reduces AI hallucination rates by grounding every agent query in semantic definitions. 66% accuracy improvement.
• •Pipeline Building Agent — Pipeline creation drops from 2-6 weeks to 2-6 hours. Clears the pipeline backlog in weeks, not quarters.
• •Data Governance and Security Agent — Access provisioning in 5 minutes instead of 5 days. Continuous compliance enforcement.
• •Real-Time Streaming Agent — Eliminates the 'one person who knows Kafka' single point of failure.
• •Swarm Orchestration Agent — Coordinates all agents, discovers undocumented dependencies, optimizes resource utilization.
• •Cost Savings and Data Cleanup Agent — Identifies the 30-40% of warehouse spend going to data nobody queries.
• •Data Migration Agent — Compresses 6-18 month migration timelines. The enterprise door-opener.
• •Agent Observability Agent — Monitors the agents themselves. Decision audit trails, drift detection, cost tracking.

Where We Are Today

We have working prototypes for our first agents and are in active design partner conversations with data teams. We are building in public, sharing our progress honestly, and looking for data engineers who want to shape how these agents work.

If you are interested in early access or want to be a design partner, reach out. No cost. Direct line to the team building this.