30 days to become a Forward Deployed Engineer (FDE)

Week 1: Build an agent that can complete a loop

1: Agent loop
prompt -> model -> response -> next step, until the task completes or a max-step limit hits

2: Tool use
one API -> one web search - agent decides when to call them

3: Guardrails
input validation - max-step limit - output filtering

4: Context & memory
context window by default, external memory only when stat must outlive the run

5: Audit trail
log every prompt, response, tool call, result, error and timestamp

6: Real workflow
run it on one process that used to be manual - tie it to a portfolio project

7: Checkpoint
a working agent with tools, guardrails, deliverate memory, and a full audit trail

Week 2: Turn the demo into a system that can recover

8: Structured outputs
is defined JSON schema, not free-form text

9: Schema validation
validates every response, retry ro escalate when the structure is invalid

10: Failure modes
missing data, malformed responses, dead APIs, timeouts, duplicates, partial completion

11: Checkpointing
save state every few steps: tools, actions, tool results, pending work, errors

12: Resume
stop the agent deliberately. restart it from the last checkpoint

13: Failure handling
explicit behavior for tool failure, bad output, incomplete state, unsafe continuation

14: Checkpoint
a resumable agent with structured outputs, state recovery, and explicit failure behavior

Week 3: Make the system measurable and economically viable

15: Retry logic
exponential backoff on every external call, 1, 2, 4, 8, stop at 16

16: Failure categories
missing context, wrong tool, wrong record, invalid output, unsafe actions, timeout

17: Golden dataset
20 real queries with hand-labeled ideal outputs - normal, edge, ambiguous, high-risk

18: Run evals
correctness, format, tool selection, required steps, escalation behavior

19: Optimize cost
cheaper models for simple subtasks, caching, token limits, cost per query

20: Multi-agent
only when decomposition help: one plans, several execute, one synthesizes

21: Checkpoint
an evaluated agent with known failure modes, measured cost, and a golden dataset

Week 4: Defend the system like an FDE

22: The pain point
who does the work, what takes time, where errors occur, what it costs

23: Why AI belongs
why not just software, what stays human, where autonomy stops

24: Architecture
stack, tools, models, data, memory, guardrails, and why each exists

25: Iterations
what v1 got wrong, what failed, what changed, how it improved

26: The evals
dataset, pass rate, failure categories, thresholds, open risks

27: Economics
time saved, errors reduced, risk, revenue, cost per query

28: Rehearse: engineer
architecture, decisions, failures, tradeoffs

29: Rehearse: VP
problem, outcome, evidence, risk - plain language

30: Final checkpoint
a complete FDE cae study for engineers and executives alike

The System
- agent loop
- two tools
- guardrails
- deliberate memory
- audit trail
- structured outputs
- schema validation
- checkpointed state
- resume after failure
- explicit failure paths
- retries + backoff
- failure taxonomy
- golden dataset
- eval suite
- cost per query
- workflow audit
- system architecture
- evaluation report
- deployment controls
- business case