#Workflow

Workflow orchestration coordinates multi-step processes with defined order, error handling, and state management. Posts cover saga patterns for distributed transactions, Microsoft Agent Framework workflow composition, and the design patterns that make long-running workflows observable, resumable, and fault-tolerant.

4 posts tagged with workflow. ← All posts

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Pratik Dhanave · ·7 min read

Patterns of Work with Agent Skills

A single skill makes an agent reliable at one task; the real power comes from how skills combine into patterns of work. An agent equipped with a library of well-designed skills can compose them, chain them, and apply them across a workflow — turning point capabilities into end-to-end reliable work. This post is about the patterns that emerge when skills work together, and how to think about an agent's skill library as a system rather than a pile of parts.

A single skill does one task; the power is in combination. Composition (many focused skills for one task — combinatorial leverage), chaining (skills across a workflow — reliable whole processes), and treating the skill library as a curated, compounding, shared system that mirrors your real work.

#Agent Skills#AI Agents#Workflow#Composition#AI Engineering
Pratik Dhanave · ·6 min read

The Machine Learning Workflow — From Problem to Production

Knowing the algorithms is maybe 20% of doing machine learning. The other 80% is the workflow around them: framing the problem, preparing the data, iterating honestly, and getting a model into production where it keeps working. This closing post assembles the series into the end-to-end process real ML follows — and names the places where projects actually fail, which are almost never the algorithm.

Algorithms are ~20% of ML; the other 80% is the workflow — framing the problem, preparing data, iterating honestly, and keeping a model working in production where it decays (concept drift). Why classical ML isn't obsolete, and why these foundations are universal to deep learning and LLMs.

#Machine Learning#MLOps#Workflow#Production#AI Engineering#Fundamentals#LLMs
Pratik Dhanave · ·6 min read

The Core Workflow

Getting great results from Claude Code is less about clever prompts and more about a disciplined loop: give context, specify clearly, let it work, review, and steer.

The disciplined loop that gets great results: explore → plan → execute → review, specifying like you'd brief a colleague, steering actively, managing context, and right-sizing delegation to the checks (tests) the agent can loop against.

#Claude Code#Workflow#AI#Productivity#AI Agents#Testing

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.