The Alignment Problem Nobody Talks About
When people say “data-driven agents,” they usually mean agents that can query databases and return results. That’s not data-driven. That’s retrieval with extra steps.
Real data-driven agents are different. They learn from their own execution. They improve based on outcomes. They connect what they did to what actually happened.
But before any of that matters, there’s a more fundamental question: what should the agent be doing in the first place?
Alignment Is the Foundation
The foundation isn’t just data quality. It’s alignment.
Every agent needs to understand its place in a larger context. Why does this company exist? What are we trying to achieve this quarter? What specific goal does this agent serve? Without this chain of alignment—mission to strategy to goals to tasks—an agent is just executing actions in a vacuum.
Consider the difference:
Without alignment: An agent monitors competitor pricing and adjusts your prices to always be 5% lower. It’s “data-driven” in the sense that it uses data. But it has no understanding of whether margin or market share matters more right now, whether this product line is strategic or being phased out, or whether a price war helps or hurts the business.
With alignment: The same agent knows the company goal is profitability over growth this quarter. It knows this product line has healthy margins. So when competitor prices drop, it evaluates whether matching makes sense given the strategic context—not just the data.
This is what we mean by agents that execute, not just inform. The execution has to be grounded in purpose.
What “Data-Driven” Actually Means
Once alignment is established, data-driven means something specific: the agent learns from its own history.
Every time an agent runs, it generates data:
- What task was it given?
- What context did it have?
- What actions did it take?
- What was the outcome?
- Did a human accept, modify, or reject its work?
This execution data is the real fuel for improvement. Not external databases—those are just inputs. The agent’s own trace history tells you whether it’s getting better or worse, which types of tasks it handles well, and where it consistently needs human correction.
Most agent frameworks ignore this entirely. They treat each run as independent. The agent has no memory of what worked before, no learning from mistakes, no accumulation of judgment.
The Feedback Loop That Matters
A truly data-driven agent has a closed loop:
- Goal — Clear task derived from aligned objectives
- Execution — Agent works on the task with full tracing
- Outcome — What actually happened (success, failure, partial)
- Feedback — Human evaluation of the work
- Learning — Agent behavior adjusts based on patterns
Most agents stop at step 2. Advanced ones reach step 3. Almost none close the loop back to step 5.
This is why we built memory and feedback directly into Squads. Every execution is logged. Feedback is captured. Patterns emerge over time. The agent doesn’t just run—it accumulates judgment.
Why This Is Hard
Building agents this way is harder than the alternative.
You can’t just wire up an LLM to your database and call it data-driven. You need:
- Clear goal hierarchies — From company mission down to specific agent tasks
- Comprehensive tracing — Every decision, every action, every outcome
- Feedback mechanisms — Ways for humans to evaluate agent work
- Learning infrastructure — Systems that surface patterns and enable improvement
Most teams skip this because it’s not the exciting part. The exciting part is watching an agent do something. The boring part is building the infrastructure that makes that agent trustworthy over time.
The Alternative Is Worse
Without alignment and feedback loops, you get agents that:
- Execute efficiently on the wrong objectives
- Repeat the same mistakes because they can’t learn
- Build no organizational trust because their reasoning is opaque
- Require constant human oversight because they never improve
This is the state of most “AI automation” today. Impressive demos that don’t survive contact with reality.
What We Actually Do
Our approach is different:
Start with alignment. Before building any agent, clarify what success looks like. Connect the agent’s task to actual business objectives. Make the goal hierarchy explicit.
Trace everything. Every agent execution generates a full trace—inputs, reasoning, actions, outputs. This isn’t optional logging. It’s the foundation for learning.
Capture feedback. When humans review agent work, that feedback is recorded. Accepted, rejected, modified—each signal improves future behavior.
Surface patterns. Over time, execution data reveals what works. Which prompts succeed? Which tasks need human review? Where does the agent consistently struggle?
Iterate deliberately. Use the patterns to improve. Adjust prompts, add guardrails, expand or restrict authority based on evidence.
This is slower than throwing an agent at a problem and hoping it works. It’s also the only way to build agents that earn trust over time.
The Real Question
When evaluating any AI agent system, ask: does it learn from its own execution?
If the answer is no—if every run is independent, if there’s no feedback loop, if the agent has no memory of what worked—then it’s not really data-driven. It’s just retrieval with better marketing.
Data-driven means the data that matters most is the agent’s own history. That’s where trust comes from.
