The End of Fragmentation

One of the hardest habits to break as a former investor is the love for best-of-breed SaaS. For a decade, the playbook was clear: buy the best ATS, the best interview tool, the best background check provider—and stitch them together with APIs. Point solutions felt sharp, focused, and easy to benchmark.

But in the AI agent era, the goal has changed. In 2025, teams aren't just buying tools to organize work. They're trying to build agent-driven workflows that run continuously—loops that execute, learn, and improve. Here's the engineering reality most stacks are about to collide with:

"You can't build a self-driving loop out of fragmented parts."

It's technically possible to layer AI on top of a fragmented stack. In practice, the integration cost often creates a fragile system because the core problem is context continuity. APIs are excellent at transferring structured fields—name, email, stage, score—but autonomous systems rely on more than fields. They rely on implicit knowledge: what signal was noticed, why it mattered, what hypothesis it implies, what to probe next, and how the system should change after new evidence.

2.1 What Fragmentation Breaks

When sourcing happens in Tool A, interviewing in Tool B, and checks in Tool C, each handoff collapses rich context into a thin interface. The "why" behind decisions gets flattened into "what." Consider a sourcing step that selects a candidate because their GitHub suggests strong architecture skills but a consistent weakness in documentation. By the time that candidate hits a third-party interview tool via API, the nuance is gone. The interview agent starts cold, asks generic questions, and misses the real signal.

2.2 The Unified Alternative

In a unified loop, agents share a single memory layer. The system carries the reason a candidate was selected into the interview, and the interview validates the exact signal that triggered sourcing. The system remembers why it picked the candidate—and uses that context to ask better questions, probe deeper weaknesses, and generate more accurate evaluations.

Foundire isn't a "feature factory." We're building what researchers call a compound AI system—a system that solves tasks using multiple interacting components (multiple model calls, retrievers, tools), rather than a single model response [4].

Hiring isn't one task. It's a debate. A Sourcing Agent proposes candidates and explains why they match. An Interview Agent tests whether those signals hold up under questioning. A Compliance Agent verifies whether there's risk that should surface early. For autonomous hiring to actually improve over time, you need a feedback loop: if the interview discovers a mismatch, the sourcing criteria must update; if a candidate passes strongly on one signal, future sourcing should amplify that pattern; if drop-off increases at a step, the workflow must adapt.

That compounding loop is difficult when each step is owned by a different vendor, each with its own memory, evaluation format, and logging.

We're moving from SaaS as a tool to service-as-software as an outcome [5]. Traditional SaaS sells you a seat so your employee can do work faster. Service-as-software sells you a result—the system executes meaningful portions of the workflow and humans supervise.

4.1 The Pain Is Measurable

Industry benchmarks make the problem concrete. iCIMS reports a global average time-to-hire of 44 days [1]. SHRM finds that 60% of job seekers abandon applications mid-process when they're too long or complex [2]. Vertical AI platforms that own the loop don't just reduce subscription sprawl—they can compress cycle time, reduce repetitive screening hours, and protect candidate experience by removing friction.

4.2 Directional Targets

Because every company and role differs, these are directional targets rather than promises: compress early-stage screening time via async interviews and structured summaries; reduce abandonment by replacing long forms with conversational flows; shift recruiters toward supervision and closing rather than repetitive triage.

A common counterargument: "We already have a data lake. We'll centralize everything, connect the tools, and the agent will have the context." This sounds reasonable—until you try to build a self-driving workflow.

5.1 The Timing Problem

Most data stacks are designed for reporting and analysis after events occur. But agentic loops need context captured at decision time: what inputs were gathered, what policy applied, what exceptions were granted, and why the system chose action A over B. Even "rich integrations" tend to move artifacts (candidate profile, transcript, score) without preserving the semantic structure of the system's reasoning—the hypothesis that drove a follow-up question, the rubric state at the time of evaluation, the uncertainty and alternatives considered, and the feedback signal that should update future behavior. That's why many teams end up with an "agent" that can summarize, but can't compound.

5.2 Continuous Evaluation

In production, agent behavior drifts. Prompts change. Models change. Data changes. And so does the distribution of candidates. A unified loop isn't "one database." It's a system that captures decision context, evaluates outputs continuously, and updates behavior in tight feedback cycles [6].

The transition isn't only technical. It's organizational. As work becomes a partnership between people and agents, teams need new roles, rituals, and accountability. McKinsey frames the future of work as collaboration between people, agents, and robots—not agents replacing humans, but reconfiguring work into partnerships [3].

In a best-of-breed world, recruiters are the "human glue" between tools. In an agentic world, recruiters become supervisors who set goals, boundaries, and escalation rules; workflow designers who define rubrics, stage gates, and what "good" looks like; quality and fairness auditors who review evidence, spot drift, and ensure consistency; and exception handlers who take the hard cases, negotiate, and close. Vertical AI wins because it supports Agent Ops natively: shared memory, shared rubric, consistent evidence trails, continuous evaluation and monitoring, clear escalation paths to humans. Fragmented stacks force humans to do this orchestration manually—which is exactly what autonomous loops are meant to remove.

"All-in-one" raises valid concerns. Here's our approach.

Interoperability. Most companies already have an ATS as the system of record. You can keep that in place, use Foundire for the agentic workflow, and export transcripts, scorecards, and summaries (PDF/CSV/shareable links) so your existing systems remain the source of truth.

Auditable fairness. Bias is real—and "AI said so" is not acceptable. Our philosophy: scoring must be tied to a rubric and grounded in evidence. When the system rates a competency, it should point to the specific parts of the interview that triggered that evaluation—so decisions can be reviewed and challenged.

Candidate experience. Respect is not optional. The system must set expectations clearly, keep tone conversational, minimize redundant questions, and ensure humans remain responsible for final decisions.

Vertical AI is powerful, but it isn't for everyone. Best-of-breed wins when you have niche regional compliance constraints, when you're deeply locked into legacy tooling, or when you only need a narrow improvement in one isolated step.

Vertical AI wins when your goal is speed, autonomy, and compounding learning; when you want a workflow that improves itself over time; and when you're optimizing the loop—not a single tool.

We're moving from a world of "humans glued together by software" to "AI agents supervised by humans." To get there, we have to stop worshiping fragmentation.

Don't just buy better tools. Build a loop that learns.