How do I evaluate an agent objectively?

Build a fixed eval set of 20–50 representative tasks with known good outputs. Run it on every change. Track pass rate, average steps, average cost. Evals are the closest thing to unit tests in this space.

If your tools will be used by multiple AI clients (Claude Desktop, Cursor, your custom app), yes. If it's a single-app integration, plain function calling is simpler.

What's the most common failure mode?

Long-horizon error compounding. 95% per-step accuracy × 20 steps = 36% overall success. Mitigations: shorter tasks, checkpointing, human-in-the-loop on irreversible actions.

Should I open-source my agent?

Probably yes — open agents accelerate the field and signal your work. Strip credentials, document the tools, write up what failed.

Learning Path · Advanced

AI Agent Builder Roadmap

The path from 'I built a chatbot' to 'I built an agent that runs unsupervised.' For builders, not lurkers.

Advanced10–12 weeks6 stages

Outcome

You've designed, built, evaluated, and shipped a real agent. You can speak credibly about tool design, eval harnesses, and failure modes.

Edited by The AIKnowHub team · Editorial team

Key takeaways

1Python or TypeScript fluency
2API integration experience
3Comfort with prompts as a first-class engineering artifact
4Patience for debugging non-deterministic systems

Recommended tools

Claude Code
OpenAI Agents SDK
LangGraph
MCP servers
LangSmith or Helicone for tracing

Projects you'll build

The roadmap

01
1. Conceptual foundation
Week 1
Don't skip this. The reason most agent projects fail is fuzzy thinking about what an agent is.
02
2. Tool design
Week 2–3
The hard part isn't the model. It's designing tools the model can actually use.
- Read: Anthropic's tool use docsdoc
- Read: OpenAI's function calling docsdoc
- Build a 3-tool agent (search + fetch + summarize)doc
03
3. Memory and context
Week 4–5
Agents that forget are agents that fail.
04
4. Eval harness
Week 6–7
Build a way to test your agent automatically. Without this you're flying blind on every change.
- Read: any post titled 'evals are the new unit tests'doc
- Build: 20-case eval set for your agentdoc
05
5. Ship a real agent
Week 8–10
Something narrow and useful. Resist the urge to build AGI in week 8.
- AI Research Assistantworkflow
- AI Email Assistantworkflow
06
6. Production concerns
Week 11–12
Latency, cost, observability, failure modes. The unsexy stuff that decides whether the agent survives contact with users.
- Cheapest AI APIscomparison
- Add tracing (LangSmith, Helicone, or your own)doc
- Set up cost monitoring and alertsdoc

Frequently asked questions

Not initially. Build the loop yourself first — it's 100 lines. Move to a framework when state management or branching demands it.

Key takeaways

The roadmap

1. Conceptual foundation

2. Tool design

3. Memory and context

4. Eval harness

5. Ship a real agent

6. Production concerns

Frequently asked questions

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