Understanding Agentic Workflows

This document explains the concepts behind agentic workflows and how the Coding Context CLI fits into this ecosystem.

What Are Agentic Workflows?

Agentic workflows are autonomous, multi-step processes executed by AI agents with minimal human intervention. They represent a shift from manual, step-by-step instructions to goal-oriented delegation.

Key Characteristics

Autonomy

  • Agents make decisions without constant human guidance
  • Agents adapt their approach based on outcomes
  • Agents can invoke tools and APIs as needed

Goal-Oriented

  • You specify what needs to be done, not how
  • Agents plan their own execution steps
  • Success is measured by outcomes, not process adherence

Multi-Step Execution

  • Complex tasks broken into subtasks
  • Each step builds on previous results
  • Agents coordinate multiple tools and services

Context-Aware

  • Agents understand project structure and conventions
  • Decisions based on repository state and history
  • Knowledge of team practices and standards

The Context Problem

AI agents are only as good as the context they receive. Without proper context, agents may:

  • Violate coding standards
  • Miss project-specific requirements
  • Ignore team conventions
  • Make inappropriate architectural decisions

Traditional Approach

Manually copy-pasting context:

❌ Copy coding standards from wiki
❌ Copy API documentation
❌ Copy relevant code snippets
❌ Paste into AI chat
❌ Repeat for each task

This is tedious, error-prone, and doesn’t scale.

Agentic Approach

Automated context assembly:

✅ Define standards once in rule files
✅ Define tasks once in task files
✅ Assemble context automatically
✅ Pipe to AI agent
✅ Reuse for all similar tasks

How Context CLI Enables Agentic Workflows

1. Standardized Context Storage

Rules and tasks are stored in version control alongside code:

.agents/
├── rules/          # Team standards, reusable across tasks
│   ├── coding-standards.md
│   ├── architecture.md
│   └── security.md
└── tasks/          # Specific workflows
    ├── code-review.md
    ├── fix-bug.md
    └── implement-feature.md

2. Dynamic Context Assembly

Context is assembled at runtime based on the specific task:

# Bug fix: Include only relevant rules
coding-context -s priority=high fix-bug

# Code review: Different context
coding-context -s stage=review code-review

3. Parameter Injection

Runtime information flows into task prompts:

# Each bug gets specific context
coding-context \
  -p issue_key=BUG-123 \
  -p description="Crashes on startup" \
  /fix-bug

4. Bootstrap for Live Data

Scripts fetch current state before agent execution:

# Fetch JIRA issue details automatically
export JIRA_ISSUE_KEY="BUG-123"
coding-context fix-bug  # Bootstrap fetches latest data

The Agentic Workflow Ecosystem 

┌─────────────────────────────────────────────────────────────┐
│                      Human Level                             │
├─────────────────────────────────────────────────────────────┤
│  Define:                                                     │
│  • Standards (rules)                                         │
│  • Tasks (what to do)                                        │
│  • Trigger conditions                                        │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│                   Context Assembly Layer                     │
├─────────────────────────────────────────────────────────────┤
│  Coding Context CLI:                                         │
│  • Discovers rules                                           │
│  • Fetches dynamic data (bootstrap)                          │
│  • Substitutes parameters                                    │
│  • Outputs formatted context                                 │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│                    Execution Layer                           │
├─────────────────────────────────────────────────────────────┤
│  GitHub Actions / CI/CD:                                     │
│  • Triggers on events                                        │
│  • Orchestrates workflow steps                               │
│  • Manages state and artifacts                               │
│  • Handles approvals and gates                               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│                      AI Agent Layer                          │
├─────────────────────────────────────────────────────────────┤
│  AI Agent (Claude, GPT, Gemini):                             │
│  • Understands context                                       │
│  • Plans approach                                            │
│  • Executes code changes                                     │
│  • Validates results                                         │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│                       Output                                 │
├─────────────────────────────────────────────────────────────┤
│  • Code changes                                              │
│  • Pull requests                                             │
│  • Documentation updates                                     │
│  • Test results                                              │
└─────────────────────────────────────────────────────────────┘

Workflow Patterns

Pattern 1: Reactive Workflows

Triggered by repository events:

# .github/workflows/agentic-code-review.yml
on: pull_request

jobs:
  review:
    steps:
      - Assemble context
      - AI reviews code
      - Post comments

Use cases:

  • Automated code reviews
  • Security scanning
  • Documentation validation

Pattern 2: Autonomous Workflows

Triggered by issues or labels:

# .github/workflows/agentic-bugfix.yml
on:
  issues:
    types: [labeled]

jobs:
  fix:
    if: contains(labels, 'bug')
    steps:
      - Assemble context
      - AI analyzes and fixes
      - Create PR with fix

Use cases:

  • Bug fixing
  • Dependency updates
  • Refactoring tasks

Pattern 3: Multi-Stage Workflows

Complex workflows with multiple phases:

jobs:
  plan:
    steps:
      - Context: planning rules
      - AI creates plan
  
  implement:
    needs: plan
    steps:
      - Context: implementation rules
      - AI writes code
  
  test:
    needs: implement
    steps:
      - Context: testing rules
      - AI validates changes

Use cases:

  • Feature development
  • Architecture changes
  • Migration projects

Benefits of Agentic Workflows

For Developers

  • Less Context Switching: Agents handle routine tasks
  • Faster Iteration: Automated feedback loops
  • Consistent Quality: Standards applied automatically
  • Focus on High-Value Work: Let agents handle boilerplate

For Teams

  • Scalable Reviews: Every PR gets thorough review
  • Knowledge Codification: Standards captured in rules
  • Onboarding: New team members learn from rules
  • Consistency: Same standards across all work

For Organizations

  • Productivity: More work completed with same team
  • Quality: Automated checks catch issues early
  • Compliance: Standards enforced automatically
  • Innovation: Developers focus on creative work

Challenges and Considerations

Agent Reliability

  • Agents can make mistakes
  • Always review agent output
  • Use approval gates for critical changes
  • Start with low-risk tasks

Context Quality

  • Garbage in, garbage out
  • Invest in quality rules
  • Keep rules updated
  • Test context assemblies

Cost Management

  • API costs for AI models
  • Monitor token usage
  • Use resume mode to save tokens
  • Optimize context size

Security

  • Protect API keys and secrets
  • Review generated code for vulnerabilities
  • Use security scanning tools
  • Maintain human oversight

Future of Agentic Workflows

Near-Term

  • More sophisticated agent capabilities
  • Better tool integration
  • Improved cost efficiency
  • Enhanced safety mechanisms

Long-Term

  • Multi-agent collaboration
  • Self-improving workflows
  • Proactive problem detection
  • Autonomous architecture evolution

Conclusion

Agentic workflows represent a fundamental shift in how software is developed. The Coding Context CLI provides the foundation for this shift by solving the critical problem of context assembly. By investing in quality rules and task definitions, teams can build increasingly sophisticated workflows that leverage AI agents effectively while maintaining quality and control.

The key is to start small, learn what works, and gradually expand the scope of autonomous operations as confidence grows.

See Also