Deesha AI · SkillLab
Agents in the Wild: Production Agent Lab
Two hands-on days taking an agent from a working demo to a production system: containerised and deployed, its state persisted across restarts, failures handled without losing work, every run observable, and the operating rules written down before real users arrive.
Designed for
- Developers whose agent works on their laptop and nowhere else
- Teams about to put an agent in front of real users or real workflows
- Engineers who have never restarted an agent mid-task and watched what happens
- Anyone responsible for an agent's behaviour at 2 a.m.
- Fee
- ₹2,700
- Duration
- 2 days · 16 hours
- Each day
- 09:00 – 17:00
- Mode
- Offline / Online
The 2 days, hour by hour
10 hands-on sessions: every one ends in a thing
Day 1
09:00 – 17:00Ship it
5 sessions09:00 – 10:15
See why laptop agents die in production
the demo-to-production gap, state and secrets audit, the plan
Takeaway Your agent's hidden assumptions listed: every path, key and in-memory state that will not survive a deployment.
10:30 – 11:45
Containerise the agent
images for agent workloads, configuration, secrets at runtime
Takeaway The agent in a container, configured from outside, with a planted secret kept out of the image and proved absent.
12:00 – 13:00
Deploy it as a service
the agent behind an API, health checks, versioned prompts and tools
Takeaway Your agent reachable as a deployed service with a health check that means something, and a version you can name.
14:00 – 15:15
Persist the state that matters
task state in a real store, resumability, what not to persist
Takeaway A long task killed halfway and resumed exactly where it stopped, because its state lives in a database, not a variable.
15:30 – 17:00
Handle the failures that will happen
tool outages, model errors, timeouts and compensation
Takeaway A tool taken down mid-task on purpose: the agent degrading, retrying and completing without corrupting anything.
Day 2
09:00 – 17:00Operate it
5 sessions09:00 – 10:15
Trace every run
run traces, step attribution, token and cost per task
Takeaway Twenty production-shaped runs on one dashboard: the expensive one found and explained in under a minute.
10:30 – 11:45
Budget and bound the fleet
concurrency and queues, rate limits, cost ceilings per task
Takeaway Fifty tasks submitted at once and handled on your terms: queued, bounded and each under its own cost ceiling.
12:00 – 13:00
Keep humans in the loop at scale
approval queues, escalation paths, audit trails
Takeaway Approvals arriving in one queue a human can actually work, with every decision recorded for the audit that will come.
14:00 – 15:15
Rehearse the bad day
incident drill, the kill switch, stopping without losing work
Takeaway A live incident drill: the agent misbehaving on cue, caught by its traces, and stopped with the kill switch you tested.
15:30 – 17:00
Write the runbook and hand it over
what to monitor, when to intervene, handover review
Takeaway A one-page runbook another engineer could operate your agent from, reviewed by the room as the final exercise.
By the end of day 2, you are holding
Your own agent running as a deployed service: containerised, its task state in a real store, resuming cleanly after a mid-task kill, its runs traced and budgeted, with an operating runbook and the escalation rules a team could actually follow.
No dates announced
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Who runs it
Practitioners, in the room with you
All trainers and mentorsEach workshop names its trainer before you book.

Vishal Shah
Founder & Principal Trainer
Two decades building and teaching commerce, banking and cloud platforms, and still writing code
- Java & Spring Boot microservices
- TypeScript, React, Angular & Next.js
- Composable commerce (commercetools)

Shrenik Shah
Principal Trainer
Cloud and AI architect who has upskilled over 5,000 engineers in Java, React, Python and cloud
- Python: Django, Flask, GenAI & agentic workflows
- Java & Spring Boot
- React & Angular, micro-frontend architecture
Running this for a team?
We deliver SkillLabs on site for institutions and engineering teams.
₹2,700
2 days · 16 hours