Process Engineering Track

Reliability is Engineered,
Not Prompted.

Stop hoping the LLM gets it right. Build the cage it works inside. Reliability comes from the environment, not the intelligence.

The Environment IS The Product

The LLM is just a CPU. The Reliability is in the motherboard.

Input Schema
Clean Signal
THE CAGE (Constraints)
The Agent
Output Schema
Retry Budget

The DIAL Framework

How to engineer a process, step by step.

Define

Write the contract first. What exactly constitutes "Done"?

Instrument

Add sensors. Measure latency, cost, and specific failure modes.

Automate

Hard-code the easy stuff. Don't ask an LLM to do math.

Loop

Feed failures back into the system to update the rulebook.

The Artifacts

Reliability isn't a feeling. It's a set of files in your repo.

1

Environment Spec

Defines the physics: Timeouts, memory, domains.

2

Runbook

Defines the steps. Explicit actions, not vague wishes.

3

Telemetry Schema

Defines the truth. How we prove it worked.

# environment.spec.yaml
# The "Physics" of the agent's world.

runtime:
  timeout_ms: 15000
  retries: 2
  memory_limit: "512mb"

constraints:
  allowed_domains: ["*.hubspot.com", "*.linear.app"]
  forbidden_keywords: ["competitor_x", "confidential"]
  
data_contracts:
  input_schema: "v2/marketing_brief.schema.json"
  output_schema: "v1/campaign_draft.schema.json"
  
failure_modes:
  on_timeout: "escalate_to_human"
  on_hallucination: "discard_and_log"

The Shift in Thinking

Prompt Engineering

  • "Please act as a marketing expert..."
  • Retry until it 'feels' right.
  • Context is pasted in manually.
  • Success is subjective (Vibes).

Process Engineering

  • "Execute runbook_v4.yaml"
  • Fail if schema check fails.
  • Context is retrieved via RAG (INDEX).
  • Success is passing unit tests.

Where this lives in the system

Reliability isn't a separate tool. It's how we implement the 4 Pillars.