Building Agentic Pipelines at Scale

The rise of agentic AI systems represents a fundamental shift in how we build software. Instead of writing deterministic code paths, we're now orchestrating autonomous agents that reason, plan, and execute complex multi-step workflows.

What Makes a Pipeline "Agentic"?

Traditional automation follows rigid, predefined paths. An agentic pipeline, by contrast, can adapt its execution strategy based on intermediate results. The agent observes its environment, reasons about the best next step, and takes action — much like a human operator would.

At Subterra, we've been building these systems for enterprise clients across industries. Here's what we've learned about making them production-ready.

Architecture Overview

Our agentic pipelines are built on three core principles:

1. Task Decomposition — Break complex goals into atomic, verifiable subtasks
2. Fault Tolerance — Every step can fail, retry, or reroute gracefully
3. Observability — Full trace logging so you can understand why an agent made each decision

Task Decomposition

The first challenge is breaking a high-level objective into manageable pieces. We use a planning agent that analyzes the goal, identifies dependencies, and creates a directed acyclic graph (DAG) of subtasks.

class PlanningAgent:
    def decompose(self, objective: str) -> TaskGraph:
        """Break an objective into a dependency graph of subtasks."""
        analysis = self.llm.analyze(objective)
        tasks = self.extract_tasks(analysis)
        return self.build_dag(tasks)

Each subtask has clear inputs, outputs, and success criteria — making the system auditable and debuggable.

Fault Tolerance

In production, things break. Network calls timeout. APIs rate-limit. LLMs hallucinate. Our pipelines handle all of this through a layered retry strategy:

• Immediate retry for transient errors (network timeouts)
• Backoff retry for rate limits
• Alternative path for persistent failures (try a different approach)
• Human escalation for ambiguous situations

Observability

Every agent decision is logged with its reasoning chain. This isn't just for debugging — it's how we build trust with enterprise clients who need to understand why their AI system made a particular choice.

Lessons Learned

After deploying dozens of agentic systems, three lessons stand out:

Start simple. The most effective agent systems we've built started as straightforward pipelines and grew more sophisticated as requirements became clear.

Test with adversarial inputs. Agents will encounter data they've never seen before. Build your test suite around edge cases, not just the happy path.

Monitor token costs. Agentic systems can burn through API credits fast if an agent gets stuck in a reasoning loop. Set hard limits and circuit breakers.

What's Next

We're working on multi-agent collaboration — systems where specialized agents hand off work to each other, negotiate priorities, and collectively solve problems that no single agent could handle alone.

If you're exploring agentic AI for your organization, get in touch. We'd love to talk about what's possible.