9.10.1 Project Roadmap: Build a Traceable Agent

An Agent project portfolio should show a traceable execution loop, not just one final model answer.

See the Project Loop First

The loop is: goal, plan, tool call, observation, state update, failure handling, stop decision, final output, evaluation.

Run an Agent Evidence Check

Use this before calling the project portfolio-ready.

project = {
    "goal_defined": True,
    "trace_saved": True,
    "tool_logs": True,
    "failure_case": True,
    "eval_tasks": 10,
}

ready = (
    project["goal_defined"]
    and project["trace_saved"]
    and project["tool_logs"]
    and project["failure_case"]
    and project["eval_tasks"] >= 5
)

print("portfolio_ready:", ready)
print("evidence:", "goal, trace, tools, failure, eval")

Expected output:

portfolio_ready: True
evidence: goal, trace, tools, failure, eval

If this says False, improve the evidence before adding more Agent roles.

Learn in This Order

Step	Project	What It Trains
1	Research assistant	Retrieval, citation, summarization, trustworthy output
2	Data analysis Agent	Python tool calls, table analysis, charts, interpretation
3	Multi-Agent development team	Role division, handoff, review loop, merge ownership
4	Hands-on workshop	The smallest traceable single-Agent baseline

Run 9.10.5 Hands-on: Build a Traceable Single-Agent Assistant before expanding the project.

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Project Goal

what the agent should accomplish and what it must not do

Baseline

single-agent loop before adding advanced features

Trace Pack

goal, plan, tool calls, observations, memory, evaluation

Failure Log

one failed or unsafe run with root cause

Deliverable

README, run command, trace screenshot/log, next step

Project Deliverable Standards

Deliverable	Minimum Requirement	Stronger Portfolio Version
README	Goal, run command, dependencies, examples	Add architecture, trade-offs, cost, safety, retrospective
Architecture	Model, tools, memory, state, evaluation, safety	Add deployment boundary and human handoff
Tool list	Callable tools, input/output schema, failures	Add permission rules and sandbox notes
Execution trace	Plan, action, observation, replan, stop	Add replayable JSONL logs
Failure case	At least 1 real failure	Add 3 cases with cause, fix, regression check
Evaluation set	Fixed tasks and pass/fail rules	Add baseline, metrics, and comparison experiments
Deployment note	How to run locally	Add API entry, environment variables, monitoring, rollback

Pass Check

You pass this chapter when another developer can replay your Agent run, inspect each tool call and observation, understand why it stopped, and see at least one failure analysis.

The basic version can be a single-Agent project. Add memory, MCP, multi-Agent collaboration, or deployment only after the trace and evaluation loop are solid.

Check reasoning and explanation

1. A passing answer describes the agent loop: goal, plan, tool call, observation, memory or state update, and stop condition.
2. The evidence should include a trace that another developer can inspect, not only the final answer.
3. A good self-check names one safety or reliability control such as tool schemas, permission boundaries, retries, evaluation cases, or a human-review point.