What is Agentic Workflows?
Design patterns for AI applications where agents iteratively plan, act, observe outcomes, and adapt strategies rather than single-pass generation. Includes reflection, self-critique, tool use, and multi-step problem decomposition for higher accuracy on complex tasks.
This glossary term is currently being developed. Detailed content covering technical architecture, business applications, implementation considerations, and emerging best practices will be added soon. For immediate assistance with cutting-edge AI technologies, please contact Pertama Partners for advisory services.
Agentic workflows automate complex multi-step business processes like vendor onboarding, competitive research, and report generation that previously required dedicated staff. These patterns reduce process completion time from days to minutes while maintaining quality through built-in verification loops. mid-market companies adopting agentic workflows effectively multiply their operational capacity without proportional headcount increases.
- Planning phase before execution vs reactive agents
- Reflection loops for self-improvement and error correction
- Task decomposition into manageable sub-goals
- Progress monitoring and dynamic re-planning
- When to use agentic vs simple prompt-completion patterns
- Begin with linear agent chains handling 2-3 sequential steps before attempting complex multi-agent orchestration patterns that multiply debugging difficulty.
- Implement cost guards that terminate workflows exceeding predefined token budgets, preventing runaway agents from consuming thousands of dollars in API calls.
- Build observability dashboards tracking each agent step's latency, token usage, and success rate to identify bottlenecks before they impact production reliability.
- Begin with linear agent chains handling 2-3 sequential steps before attempting complex multi-agent orchestration patterns that multiply debugging difficulty.
- Implement cost guards that terminate workflows exceeding predefined token budgets, preventing runaway agents from consuming thousands of dollars in API calls.
- Build observability dashboards tracking each agent step's latency, token usage, and success rate to identify bottlenecks before they impact production reliability.
Common Questions
How mature is this technology for enterprise use?
Maturity varies by use case and vendor. Consult with AI experts to assess production-readiness for your specific requirements and risk tolerance.
What are the key implementation risks?
Common risks include technology immaturity, vendor lock-in, skills gaps, integration complexity, and unclear ROI. Pilot programs help validate viability.
More Questions
Assess technical capabilities, production track record, support ecosystem, pricing model, and alignment with your AI strategy through structured proof-of-concepts.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
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