Hiring managers reviewing harness engineering candidates see dozens of chatbot demos every week. A chatbot that calls an API and returns a response proves you can follow a tutorial. It doesn’t prove you can build systems that work reliably in production.
What separates hired candidates from rejected ones isn’t the sophistication of their agent logic. It’s the infrastructure around it. Retry logic that handles API failures. Output validation that catches hallucinations. Cost tracking that prevents runaway spending. Evaluation pipelines that measure quality over time. These are the skills companies pay for.
This guide covers three portfolio projects that demonstrate production harness engineering skills, how to present them, and what hiring managers actually look for when reviewing portfolios.
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What hiring managers look for
Before building projects, understand what evaluators care about. Interviews with hiring managers at companies deploying AI agents reveal consistent priorities.
Production thinking, not prototype thinking. Can the candidate anticipate failure modes? Do they build systems that handle errors gracefully? Do they think about cost, latency, and observability from the start? A portfolio project that includes “what happens when the API is down” shows production thinking. One that assumes everything always works shows prototype thinking.
Measurement and evaluation. Can the candidate measure whether their agent works? Projects with evaluation datasets, quality scores, and pass/fail metrics demonstrate systematic engineering. Projects without measurement demonstrate hope.
Cost awareness. Does the candidate think about efficiency? Model tiering, caching strategies, and token optimization show that the candidate understands the economics of agent systems. This matters because unoptimized agent systems can cost 10x more than they should.
Clear documentation. Can the candidate explain what they built and why? A well-documented README with architecture diagrams, setup instructions, and design decisions is itself a signal of engineering quality.
Project 1: Fault-tolerant research agent with harness
What to build: An agent that takes a research question, searches the web, synthesizes sources, and produces a cited answer. The agent itself is straightforward. The harness around it is what makes this a portfolio piece.
The harness components to include:
- Retry logic with exponential backoff for API failures (search API, LLM API)
- Output validation that checks citations against retrieved sources (hallucination detection)
- Cost tracking that logs per-query token usage and API costs
- Circuit breaker that stops retrying after consecutive failures and returns a graceful error
- Structured logging that records every step for debugging
Architecture diagram:
User Query → [Input Validation] → [Search Tool] → [Retry Wrapper]
↓
[Source Retrieval] → [Context Assembly] → [LLM Call] → [Retry Wrapper]
↓
[Output Validator] → [Citation Checker] → [Response]
↓
[Cost Tracker] → [Logger]
What makes this stand out: The citation checker that validates whether the agent’s claims are actually supported by the retrieved sources. This demonstrates that you understand hallucination as a production problem, not just a theoretical concern.
Time to build: 2-3 weeks part-time.
Project 2: Evaluation pipeline with CI/CD integration
What to build: An evaluation framework that tests an agent against a dataset of 100+ examples, with three types of grading and CI/CD integration.
Components to include:
- Evaluation dataset with 100+ examples across multiple categories, difficulty levels, and failure modes
- Deterministic grading (tool call validation, schema checks, constraint verification)
- Model-based grading with specific rubrics (not “is this good?” but “does this summary capture the three main points?”)
- Score tracking that compares results across versions
- CI/CD integration that runs the eval suite on every prompt change and gates deployment on minimum quality scores
Dataset structure:
| Category | Count | Difficulty | Example Type |
|---|---|---|---|
| Factual questions | 30 | Easy-Medium | Test accuracy |
| Multi-step tasks | 25 | Medium-Hard | Test planning |
| Adversarial inputs | 20 | Hard | Test robustness |
| Edge cases | 15 | Hard | Test boundary handling |
| Constraint tests | 10 | Medium | Test compliance |
What makes this stand out: The CI/CD integration. Showing that your evaluation pipeline runs automatically and prevents quality regressions from reaching production demonstrates that you understand how verification fits into a development workflow. Read our evaluation dataset guide for methodology.
Time to build: 3-4 weeks part-time.
Project 3: Multi-agent system with monitoring dashboard
What to build: A system where two agents collaborate (a research agent and a synthesis agent), with full observability infrastructure.
Components to include:
- Two specialized agents that communicate through a coordinator
- Structured logging with correlation IDs that trace requests across both agents
- Metrics dashboard showing latency per agent step, token usage, cost per query, and quality scores
- Alerting rules that flag latency spikes, quality drops, or cost overruns
- Cost controls with per-query budgets and model tiering
Dashboard panels to include:
- Real-time: active queries, latency p50/p95/p99, error rate
- Quality: evaluation score trend, failure mode distribution
- Cost: cost per query over time, model tier distribution, cache hit rate
- Operations: agent utilization, queue depth, correlation ID lookup
What makes this stand out: The monitoring dashboard. Most portfolio projects focus on the agent logic. Adding production observability shows that you think about operating systems, not just building them. For patterns on multi-agent coordination, see our design patterns guide.
Time to build: 4-6 weeks part-time.
How to present your portfolio
GitHub repository structure
Each project should have a clean, professional repository:
project-name/
README.md # Architecture, setup, design decisions
src/ # Clean, well-organized source code
tests/ # Unit tests for harness components
eval/ # Evaluation dataset and grading scripts
docs/ # Architecture diagrams, API docs
.github/workflows/ # CI/CD pipeline (bonus points)
README essentials
Your README is the first thing a reviewer reads. Include:
- What this does (2-3 sentences, not a paragraph)
- Architecture diagram (showing harness components clearly)
- Key design decisions with reasoning (why exponential backoff instead of fixed retry? Why this evaluation rubric?)
- Setup instructions (working in under 5 minutes)
- Example output (show the agent working, including error handling)
- What I’d improve (shows self-awareness and growth mindset)
Live demo (optional but powerful)
If you can deploy your project publicly (even on a free tier), a live demo is the strongest signal. A reviewer who can interact with your agent and see the monitoring dashboard is far more convinced than one who reads your code. Use a platform like Railway, Render, or a free-tier cloud VM.
Common portfolio mistakes
Mistake 1: All agent, no harness. A portfolio of chatbot demos doesn’t demonstrate harness engineering. Every project must include at least three harness components (retry logic, output validation, cost tracking, observability, evaluation).
Mistake 2: No evaluation data. Projects without evaluation datasets look like prototypes. Include at least 50 test cases with grading criteria and pass/fail results.
Mistake 3: Overcomplicating the agent. The agent logic should be simple so the harness infrastructure stands out. A research agent with web search is sufficient. You don’t need a 10-tool, 5-agent system to demonstrate harness skills.
Mistake 4: No documentation. Hiring managers spend 5-10 minutes per portfolio review. If they can’t understand your project from the README, they move on. Invest 20% of your project time in documentation.
Mistake 5: Copy-pasting tutorials. If your project looks identical to a LangChain tutorial with minor modifications, it shows tutorial-following skills, not engineering skills. The harness layer should be your original work.
Timeline from zero to portfolio-ready
| Week | Activity |
|---|---|
| 1-3 | Build Project 1 (research agent with harness) |
| 4-6 | Build Project 2 (evaluation pipeline) |
| 7-10 | Build Project 3 (multi-agent with monitoring) |
| 11 | Polish READMEs, architecture diagrams, deploy demos |
| 12 | Start applying while continuing to improve projects |
This timeline assumes 15-20 hours per week of focused work. For a complete learning roadmap that includes portfolio building, follow our 6-month roadmap.
Frequently asked questions
How many portfolio projects do I need?
Three is the sweet spot. One project can be luck. Two might be pattern-matching. Three projects covering different harness skills (reliability, evaluation, observability) demonstrate breadth and consistency.
Should I use frameworks like LangChain in my portfolio?
Use frameworks where they add value, but make sure the harness layer is clearly your own work. A project that uses LangGraph for orchestration but adds custom retry logic, evaluation, and monitoring demonstrates that you understand both frameworks and infrastructure.
Do I need to deploy my projects?
Not required, but deployed projects are significantly more impressive. Even deploying on a free tier shows that you can move from code to production. A monitoring dashboard running on real (even simulated) traffic is more convincing than screenshots.
How do I explain my portfolio in interviews?
Lead with the production challenges, not the agent features. “I built a research agent” is a weak opener. “I built a harness that catches hallucinated citations before they reach users, with retry logic that handles API failures and a cost tracking system that logs per-query spending” demonstrates the right skills.
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