Insights from Hyoun Park, CEO & Principal Analyst at Amalgam Insights, on fixing context gaps and building dependable AI agents

Listen now on YouTube | Spotify | Apple Podcasts

*The Data Faces Podcast with Hyoun Park, CEO & Principal Analyst at Amalgam Insights*

When Getting the Facts Right Isn’t Enough

Your research agent delivers a comprehensive competitive landscape in hours—but misses three rising startups that could disrupt your market.

A sentiment-analysis bot at a hedge fund buys shares based on a viral post no one can verify. When the correction hits, the gains evaporate.

A sales-procurement agent negotiates what looks like a win-win contract, but inserts risky legal language that your lawyers catch only after signing.

Hyoun Park calls these agentic-quality failures—moments when an agent hits the factual notes but completely misses the real goal.

We tend to benchmark AI on facts alone, and that isn’t how agents succeed in the real world.
Hyoun Park

So what does good look like? That’s where agentic quality comes in.

About the speaker

Hyoun Park is CEO and Principal Analyst at Amalgam Insights. Over 17 years as an analyst—starting at Aberdeen Group—he has helped Fortune 500 firms turn data investments into business value.

What you’ll learn in this post

Why the word hallucination sends AI teams off course
How to spot and measure agentic quality
Why vector stores and synthetic data are merely baseline plumbing
How to rein in agent sprawl before it buries signal in noise
Why real-time trust scores will be the next competitive edge

https://www.youtube.com/watch?v=QQmbSmb__cE

The “Hallucination” Myth Is Holding You Back

Teams love labeling weird AI output as a “hallucination.” It sounds scientific and mysterious. But here’s the problem: it sends you hunting for ghosts in the neural network instead of fixing obvious gaps in context or goals. Frankly, I call it plain ole BS.

Calling it a hallucination makes it sound magical; it’s really the model showing the limits of its context. — Hyoun Park

Two Examples That Make This Real

The Contract Risk That Wasn’t Magic

A sales-procurement agent merged template clauses and slipped risky legal language into what should have been a standard contract. The team blamed “hallucination” and started digging into model weights. The actual fix? Adding a simple legal-risk flag to the prompt.

The Viral Post That Fooled the Bot

A sentiment-trading algorithm bought shares based on a viral social media post that no one had validated. Again, the team cried “hallucination.” The real gap? A missing credibility filter that would have caught unverified sources.

The Better Approach

Treat off-track output as a context mismatch, not a mystery. Ask these questions first:

What context did the agent lack?
Is the user goal crystal clear?
Are the guardrails specific enough?

Small prompt edits or sharper data retrieval rules usually solve the problem faster than a deep dive into model internals.

What Agentic Quality Actually Means

Most teams still judge agents like search results: if the facts look right, they pass. Hyoun pushes a different measuring stick.

Agentic quality is whether the agent actually gets the job done in the context it was given. — Hyoun Park

The Agentic Quality Checklist

Four checkpoints make that yard-stick concrete.

Finish the goal: Did the agent complete the task without wandering off into tangents?
Adapt when facts change: When new data appeared mid-task, did the agent revise its approach or plow ahead on an outdated path?
Respect context limits: Did it pull current, relevant information while staying inside business and legal boundaries?
Set up the next step: Does the final output make it obvious what you or your system should do next?

A Real-World Example

Box’s demo agent mapped an entire vendor landscape in hours and automatically flagged missing categories when the initial prompt skipped them. It finished the job, adapted to new cues, used the right data, and made the next decision obvious.

That’s agentic quality in action.

Why Accuracy Scores Miss the Point

BLEU scores and accuracy metrics look reassuring on dashboards. But they miss something critical: how does your agent think when data shifts mid-task?

We still don’t have a benchmark for how the agent thinks once the data moves under its feet. — Hyoun Park

What the usual scores miss

They grade answers, not decision paths: You see the final output but miss the reasoning chain that led there.
They ignore timing: Slow but correct still gets a gold star, even if speed matters for your use case.
They overlook partial wins: An agent that completes 80% of a complex task before stalling gets the same score as one that fails immediately.

A quick audit loop that works now

Log every decision node and tag each step as win, partial, or mismatch
Review weekly with the product owner and a data engineer
Tighten prompts or add fallback rules, then rerun the task
Measure improvement over time, not just final accuracy

This builds a habit of checking real outcomes instead of staring at tidy charts.

Your Vector Stores Are Just Plumbing Now

Remember when vector databases felt like secret weapons? Those days are over.

Vector search is a commodity feature now. If your strategy stops there, you’re already behind. — Hyoun Park

Why these tools became standard

Privacy regulations push teams to generate synthetic data instead of using real customer records.
Every major platform now bundles vector search for fast semantic lookup.
Low-code generators can spin up millions of fake data rows in minutes.

The Hidden Risks

Synthetic data can mask edge cases that break your system in production.
Test data lingers and bloats storage when teams forget cleanup.
Vector retrieval serves stale content if you skip freshness checks.

How to get real value

Treat these as baseline infrastructure, then look elsewhere for differentiation
Shadow-test agents on de-identified live traffic before full rollout
Feed user feedback into oversight dashboards—quality gains now come from monitoring, not storage tech

Agent sprawl will bury your quality signals

Picture this: next quarter, every department can spin up its own AI agent in minutes. Sales creates one for lead scoring. Finance builds another for expense approvals. Marketing launches three for content generation.

Sound exciting? It should also sound terrifying.

If you don’t know who owns the logic, your quality signals vanish in the noise. — Hyoun Park

Why sprawl happens fast

Low-code tools let business analysts clone agents without IT review.
Each team tweaks prompts and data access with no shared playbook.
Old bots stay online because nobody feels responsible for shutting them down.

The governance nightmare

Orphaned agents keep processing sensitive data after their owners leave the company.
Redundant bots fire conflicting updates that confuse your dashboards.
Audit teams stare at thousands of logs with no common schema to make sense of them.

How to stay in control

Create an agent registry that lists each bot’s purpose, owner, and data permissions.
Run quarterly sunset meetings to merge duplicates or retire unnecessary agents.
Standardize logging with timestamp, agent ID, and success tags so quality rolls up into one clear view.

These steps prevent agent chaos before it starts.

Real-time trust scores are your next competitive edge

Here’s the question that will define enterprise AI in 2025: can you trust an agent’s next move in real time?

We still lack a live score that tells you an agent’s next step is worth the risk. — Hyoun Park

Why this matters now

Picture an invoice-approval agent that can release payments automatically. One wrong approval triggers fraud. Without a quick trust signal, your finance team faces an impossible choice: block automation entirely or accept hidden risk.

Why you need live trust signals

Agents trigger real-world actions like payments, orders, and contract approvals with minimal human review.
Vector grounding fixes facts but doesn’t prove the action fits your business goal.
Clear trust scores let risk teams set limits before damage happens, not after.

Early Techniques That Build Confidence

Hash every prompt and response so reviewers can replay the decision chain instantly.
Give each agent a permission token that expires after one task to prevent silent reuse.
Attach signed receipts to every external API call for an unalterable audit trail.

Leaders who standardize trust checks now will build confidence into daily operations. Teams that wait will scramble after the first bad headline.

What you can do this month

Shift the conversation from model magic to mission success—that’s the essence of agentic quality. — Hyoun Park

Agentic quality shows up when an agent meets your goal within real constraints of data, time, and risk. You don’t need a research lab to start acting on this idea.

Your Four-Week Action Plan

Week 1: Define success in plain language: Work with the business owner who feels the pain every day. Write one sentence that describes what “done right” looks like.
Week 2: Pick your pilot: Choose one workflow worth at least $50K annually where an agent can help. Start with something that breaks often or takes too long.
Week 3: Turn on decision logging: Track every agent choice for two weeks. Tag each step as win, partial, or mismatch. Don’t worry about perfect categories—just start collecting data.
Week 4: Fix and measure: Review logs with your team. Fix obvious prompt gaps or data retrieval misses. Set a follow-up date to measure improvement.

Small loops like these build the habit of checking real outcomes instead of chasing accuracy scores. Teams that practice goal-based reviews now will stay ahead as agents spread through the rest of your tech stack.

About David Sweenor

David Sweenor is an AI, Generative AI, and Product Marketing Expert. He brings this expertise to the forefront as founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Podcast Highlights – Key Takeaways from the Conversation

0:00 – 1:05 | The hallucination trap
Why calling bad outputs “hallucinations” sends teams digging into neural networks instead of fixing missing context.

7:15 | The four-point quality check
Hyoun’s practical framework: finish the goal, adapt to changes, respect limits, enable next actions.

11:00 | Box’s winning demo
How one agent mapped an entire vendor landscape and flagged gaps—showing goal completion and context awareness.

17:10 | Why accuracy scores fail
Current benchmarks miss how agents think when data shifts mid-task.

20:45 | Vector search is table stakes
These tools remain useful but no longer differentiate—the edge comes from oversight and feedback.

25:10 | The agent sprawl crisis
How low-code tools will spawn thousands of bots without proper governance.

30:55 | Trust scores as competitive moat
Why real-time confidence signals will separate leaders from followers.34:25 | Start this week
Hyoun’s practical four-week plan to build agentic quality into your operations.