The Illusion of Infinite Agency

Everyone wants agents that “just work.”

The vision is seductive: you describe your business task in natural language, and a fleet of LLM-powered agents orchestrates APIs, transforms data, and auto-generates the perfect dashboard. No code. No setup. No infrastructure.

Companies like OpenAI, Anthropic, and Google DeepThink have all released tools and frameworks that push toward this dream—where agentic systems can reason, reflect, and adapt dynamically across domains. These systems are general, reusable, and often impressive in narrow demos.

But when you're automating mission-critical workflows inside enterprise systems—SAP, Salesforce, Oracle, IBMi, or NetSuite—the reality is harsher: unbounded agentic freedom becomes brittle, expensive, and often just plain wrong.

At Turgon, we’ve felt this pain firsthand. So we made a deliberate choice:

👉 We built our own agent orchestrator from the ground up.

Not to replace foundation models—but to wrap AI in constraints, scaffolding, and purpose-built control mechanisms. Here's why.

The Agency–Control Tradeoff Curve

At the heart of any AI Agent system is a tension:

• More control means more predictability and fewer errors—but less adaptability.
• More agency means more flexibility and creativity—but more room for hallucinations, missteps, and subtle failures.

This tradeoff is not theoretical. It’s measurable. It’s real. And probabilistic, non-deterministic outcomes are at the very core of large language model technology.

Let’s say you're building a reporting pipeline from IBMi mainframes to modern visualization tools like Hex. A general-purpose agent might:

• Explore all possible architectures (Parquet? Delta? Iceberg?),
• Try building schemas from raw journal entries,
• Suggest using Airflow, or Snowpark, or Google Sheets as an orchestration layer (!).

Yes, modern AI agents can explore different reasoning paths in parallel—a computationally expensive but logic-rich approach—but with five to ten options at each step, one can expect millions of permutations of reasoning paths when extended across hundreds of steps.

That’s a lot of compute, especially when each permutation will also need to be evaluated with Reflexion agents and eval loops to even get something approximately correct.

But in practice, we already know the right blueprint:

✅ Use AWS S3 as a durable landing zone (data lake)
✅ Apply the Bronze → Silver → Gold Medallion architecture
✅ Load into Snowflake as warehouse
✅ Use dbt for SQL transformations
✅ Use Dagster to orchestrate the whole pipeline

There is zero need to "give agency" to the agent to reinvent these choices.

Instead, we use code to lock the architecture and give the agent freedom only where it's needed—like:

• Understanding idiosyncratic table structures in IBMi,
• Mapping legacy codes to usable metrics,
• Generating semantic layer models from historical report usage,
• Debugging failed joins or edge-case anomalies.

Control where we know the path. Agency where we don’t.

The Risk of Overconfident Errors

Unbounded agentic systems often suffer from a dangerous pattern:

✨ Amplified Conviction + Probabilistic Error = Confidently Wrong Decisions

The agent doesn’t just hallucinate—it believes it’s right. Worse, it writes code, deploys scripts, or mutates data pipelines with this false certainty.

There are mitigations:

• Evals to measure accuracy,
• Feedback loops to ground responses,
• Reflection agents that double-check reasoning,
• Reflexion loops that self-heal failures.

But these add latency, cost, and uncertainty. More tokens, more runs, more compute. And still, no guarantee of correctness.

For a large enterprise moving petabytes across systems, “maybe correct” is not good enough.

Turgon’s Approach: Pragmatic Agentic Infrastructure

At Turgon, we’ve built a hybrid system that:

• Defines control surfaces in code: pipeline architecture, warehouse design, orchestration, and security are all declarative.
• Assigns scoped agency to agents: agents explore and adapt to enterprise-specific data structures, formats, and anomalies.
• Wraps every agent action with observability and rollback layers.
• Orchestrates agents via our own runtime, built to manage multi-agent coordination across complex workflows like ETL, reporting, and monitoring.

This design lets us:

• Deliver working pipelines in days, not weeks,
• Reduce cost by minimizing retries and reflection cycles,
• Offer high reliability that general-purpose agents cannot match.

And crucially: we do it without asking the agent to become a systems architect.

What We Learned

Building your own orchestrator isn’t about ego. It’s about owning the fault surface.

By choosing what the agent can and cannot control, we shrink the space of possible failure. We focus human creativity (and LLM power) where it matters most—on the enterprise unknowns, not the architectural knowns.

We don’t believe in over-automation or magical autonomy. We believe in cooperative automation, where agents are tools—not gods.

So yes, OpenAI agents might wow in a demo. But at Turgon, we’re shipping production pipelines that actually work—safely, affordably, and fast.

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