Agentic software development workshops are moving beyond prompt demonstrations. The useful format in 2026 is a build-focused lab where developers learn to design software agents that can plan tasks, call tools, inspect results, recover from errors, and operate within clear human controls.
For Indian startups, engineering teams, colleges, and independent builders, the goal should be practical: leave with a working prototype, an evaluation method, and a repeatable workflow—not just a collection of generated code snippets.
What an agentic software development workshop should teach
An agentic system is software that can pursue a defined objective through multiple steps. It may interpret a request, select tools, retrieve information, write or modify files, run tests, and ask for approval before taking a consequential action.
A workshop should distinguish this from ordinary AI-assisted coding. A coding assistant completes a function or explains an error. An agentic workflow manages a broader task with state, tools, decision rules, and feedback loops.
Participants should learn to define:
- The task boundary: What the agent is allowed—and not allowed—to do.
- The tools: APIs, databases, browsers, terminals, repositories, or internal services it can access.
- The state: What information persists between steps and how it is logged.
- The success criteria: Tests, business rules, quality thresholds, or human approval.
- The failure path: What happens when a tool times out, data is missing, or the model is uncertain.
Teams planning broader automation can pair the workshop with guidance on best practices for developing agentic workflows, particularly around permissions, observability, and evaluation.
A practical workshop structure
A one-day workshop can work for an experienced engineering team, while a two- or three-day format gives participants time to build and evaluate a complete system.
1. Define a useful problem
Start with a workflow that is narrow, measurable, and relevant to the participants. Good examples include:
- Triaging GitHub issues and proposing labels or owners.
- Reviewing pull requests against a defined checklist.
- Turning a product brief into tickets, acceptance criteria, and test cases.
- Searching internal documentation and citing the source of each answer.
- Monitoring a business process and escalating exceptions to a human.
Avoid vague challenges such as “build an autonomous developer.” A bounded task makes it easier to measure accuracy, cost, latency, and risk.
2. Map the workflow before selecting a model
Have participants draw the current process and mark decisions, data sources, approvals, and failure points. Then identify which steps need language reasoning and which are better handled by deterministic code.
This prevents a common mistake: using an AI agent for simple validation, arithmetic, access control, or business rules that should remain explicit in software.
3. Build a minimal tool-using agent
The first implementation should use a small number of tools and a clear loop:
1. Receive a structured task.
2. Select an approved tool.
3. Validate the tool arguments.
4. Execute the call in a controlled environment.
5. Inspect the result.
6. Continue, stop, or request human approval.
Participants can then add retrieval, memory, structured outputs, retries, and multi-agent coordination only when the use case demands them. For web-focused exercises, a comparison of AI tools for web development in India can help teams assess speed, integration effort, and local deployment considerations.
Core exercises to include
Tool calling and permissions
Give the agent a read-only search tool first, then introduce a write action behind approval. Demonstrate why tool schemas, input validation, rate limits, and scoped credentials matter. Never provide workshop agents unrestricted production access.
Retrieval and source grounding
Ask participants to build a documentation assistant that returns citations and declines to answer when evidence is insufficient. This exposes the difference between fluent output and reliable output.
Code generation with tests
The agent should propose a change, run tests, inspect failures, and produce a patch for review. Require human approval before merging or deploying. Participants interested in automating broader development processes can extend this exercise using ideas from how to automate web development with generative AI.
Failure injection
Deliberately create malformed tool responses, API timeouts, conflicting documents, prompt-injection attempts, and incomplete requirements. The objective is to teach recovery and safe termination—not to hide failure.
Evaluation
Prepare a small test set before the build begins. Score task completion, factual accuracy, tool-selection accuracy, unnecessary actions, latency, token usage, and escalation quality. Record traces so participants can inspect where a run went wrong.
A sensible technology stack
The stack should match the participants’ experience and the deployment constraints. A typical workshop may use:
- Python or TypeScript for orchestration.
- A model API with structured tool calling.
- A lightweight database for task state and audit logs.
- An isolated container or sandbox for code execution.
- GitHub or another version-control platform.
- A tracing and evaluation layer for run inspection.
The specific framework matters less than the architecture. Teach participants to keep prompts, tools, policies, model settings, and evaluation data versioned separately. If the workshop is aimed at enterprise teams, discuss deployment, data residency, access control, and integration with existing identity systems. Enterprise AI app development platforms in India offer useful comparison points for teams weighing managed platforms against custom orchestration.
Safety and governance checklist
Every project should include controls from the first prototype:
- Use synthetic or redacted data during training.
- Apply least-privilege credentials and separate development from production.
- Require approval for payments, external messages, code merges, deletions, and regulatory actions.
- Log prompts, tool calls, outputs, errors, and approvals without exposing sensitive secrets.
- Add timeouts, retry limits, spending limits, and loop detection.
- Test for prompt injection, data leakage, insecure code, and unauthorized tool use.
- Provide a manual stop mechanism and a clear incident process.
For Indian deployments, teams should also review contractual obligations, sector-specific requirements, privacy controls, and where data is processed. A workshop is an ideal setting to make these decisions explicit rather than leaving them to a later production launch.
How to choose or design a workshop
Before enrolling or commissioning a programme, ask for:
- A published agenda with build time, not only lectures.
- Prerequisites for coding, APIs, Git, and cloud access.
- A defined capstone and sample evaluation rubric.
- Instructor experience operating AI systems, not just demonstrating them.
- Guidance on costs, model quotas, data handling, and post-workshop support.
- A take-home repository with tests, traces, and deployment notes.
For early-career developers, a workshop can be paired with remote open-source software development internships in India so participants continue improving through real review and collaboration. Teams comparing conversational systems may also benefit from understanding the difference between a voice agent and a chatbot before selecting a capstone.
What success looks like
A successful agentic software development workshop produces a small system that is understandable, testable, and safe to change. Participants should be able to explain why the agent used each tool, identify when it should have stopped, reproduce a failed run, and improve performance using evidence.
The strongest outcome is not maximum autonomy. It is a reliable division of labour: deterministic software handles rules and controls, models handle bounded reasoning, and people retain authority over high-impact decisions.