agentic-sequential

name	agentic-sequential
description	Sequential workflow pattern for deterministic, fixed-path tasks. Use when solution steps are known upfront and follow a stable repeatable sequence.

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name: "agentic-sequential" description: "Sequential workflow pattern for deterministic, fixed-path agentic tasks. Use when solution steps are known upfront and the process follows a stable, repeatable sequence."

Agentic Sequential Workflow Pattern

[!TIP] TL;DR: Use this pattern when the full step-by-step process is known before execution. Best for deterministic workflows like invoice processing, employee onboarding, or data pipelines. Key Principle: "If the process is deterministic, the agent should execute — not decide."

Pattern Overview

The sequential workflow pattern handles tasks with a known solution path. The agent follows explicit steps in order, passing outputs from one stage to the next until completion.

┌─────────┐    ┌─────────┐    ┌─────────┐    ┌─────────┐
│ Step 1  │───▶│ Step 2  │───▶│ Step 3  │───▶│ Step N  │
│ Output  │    │ Output  │    │ Output  │    │ Final   │
└─────────┘    └─────────┘    └─────────┘    └─────────┘

Decision Gate

When to use: Apply this pattern when the answer to Question 1 is YES:

• "Is the solution path known in advance?"

Good fit examples:

• Invoice processing: extract fields → validate → store → confirm
• Employee onboarding: create accounts → send welcome email → assign manager → schedule orientation
• Document processing: parse → extract → transform → load

Critical Design Choice

What to use	Where to use it
LLM/Agent	Interpretation, generation, complex decisions
Deterministic code	Routing, data transformation, storage, validation

Rationale: This separation keeps systems fast, predictable, and cost-efficient. The agent handles ambiguity; code handles repetition.

Failure Mode: Over-Engineering

The main failure signal is adding ReAct-style reasoning where every step is already defined.

❌ Over-Engineered Example

Agent decides: "Now I should validate the email format..."
Agent decides: "Now I should check if the email exists..."
Agent decides: "Now I should send the welcome..."

✅ Correct Approach

# Deterministic code handles the workflow
for step in ["validate_email", "check_existence", "send_welcome"]:
    execute_step(step)
# Agent only called for steps requiring judgment
if step.requires_ai():
    agent.execute(step)

AGEM Integration

Native Tools

• run_agem_cycle — Execute each step in sequence
• get_agem_state — Monitor step completion
• spawn_agem_agent — Use specialized agents for steps requiring different reasoning styles

MCP Server Usage

• mcp-logic — Prove step dependencies and ordering constraints
• sheaf-consistency-enforcer — Register each step's output as agent state for consistency tracking

Workflow Template

1. Define ordered step list with clear inputs/outputs
2. For each step:
   a. Check if deterministic → execute with code
   b. Check if requires AI → run_agem_cycle for that step
   c. Validate output before proceeding
3. On edge case failure → escalate to Question 2b pattern

Escalation to Other Patterns

When to escalate to other patterns:

Signal	Next Pattern
Steps require external data	→ Add Tool Use
Edge cases emerge requiring new steps	→ → Question 2b (adaptive)
Different steps need different reasoning	→ → Multi-Agent

Quick-Start Template

## Sequential Workflow Definition

**Task**: [Task name]
**Path Known**: [YES/NO - if NO, use other pattern]

### Steps (ordered):
1. [Step 1] → expects: [X], produces: [Y]
2. [Step 2] → expects: [Y], produces: [Z]
3. [Step N] → expects: [...], produces: [final]

### AI Steps: [Which steps need agent reasoning]
### Code Steps: [Which steps are deterministic]

### Escalation Triggers:
- [Edge case condition] → escalate to [pattern]

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See Also

• agentic-react — For adaptive step-by-step reasoning
• agentic-planner — For tasks with upfront planning needs
• agentic-multiagent — For multi-stage tasks with specialization