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WorkshopRuns on requestAI & Agentic AIAgentsDeploymentState persistenceObservabilityDocker

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
Request a schedule

The 2 days, hour by hour

10 hands-on sessions — every one ends in a thing

Day 1

09:00 – 17:00

Ship it

5 sessions

09:00 – 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 – 17:00

Operate it

5 sessions

09:00 – 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.

Request a schedule

Agents in the Wild — Production Agent Lab

Runs on request, for individuals and for teams.

How will you join?

Laptop ready with the prerequisites?

Opens WhatsApp with a message naming this workshop. We confirm your seat and payment by reply — this website stores nothing.

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Who runs it

Practitioners, in the room with you

All trainers and mentors

Each workshop names its trainer before you book.

  • Vishal Shah

    Vishal Shah

    Founder & Principal Trainer

    Two decades building and teaching commerce, banking and cloud platforms — still writing code

    • Java & Spring Boot microservices
    • TypeScript, React, Angular & Next.js
    • Composable commerce (commercetools)
    Full profile →
  • Shrenik Shah

    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
    Full profile →

Running this for a team?

We deliver SkillLabs on site for institutions and engineering teams.

Talk to Deesha

₹2,700

2 days · 16 hours

Request a schedule