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Introduction
The AI agent landscape evolves at breakneck speed—prompt engineering gave way to harness engineering, evals are replacing guesswork, and production feedback loops now separate real impact from hype. For cybersecurity professionals, the challenge isn’t just keeping up; it’s cutting through the noise to adopt practical, battle-tested methods that actually work in domains like threat hunting, incident response, and compliance.
Learning Objectives
- Understand the core concepts of harness engineering, evaluation frameworks, and feedback-driven development for AI agents.
- Build, test, and iterate on security-focused agents using open-source tools, containerization, and real-world data.
- Apply these techniques to harden agent deployments, integrate with existing security stacks, and create measurable improvements.
1. Harness Engineering: The Backbone of Agentic Systems
Harness design, as outlined by Anthropic, provides a structured environment where long-running agents can execute reliably. For security agents, this means defining clear interfaces, managing state, and capturing every action for later analysis.
Step‑by‑Step Guide
- Define the agent’s scope – e.g., a threat-hunting agent that queries SIEM logs and generates alerts.
- Create a harness directory with configuration files, logging, and environment variables.
- Use Docker to containerize the agent for consistency across development and production.
Code Snippet (Linux)
Create harness structure
mkdir -p security-agent/{config,logs,src}
cd security-agent
touch config/env.json logs/agent.log src/agent.py
Dockerfile Example
FROM python:3.11-slim WORKDIR /app COPY src/ /app/src COPY config/ /app/config RUN pip install openai langchain elasticsearch CMD ["python", "src/agent.py"]
Explanation
The harness isolates the agent, allowing you to inject mock data, replay logs, and experiment without affecting production systems. This approach is critical for testing agent behavior in security environments where false positives can have serious consequences.
2. Building Evals for Deep Agents
Harrison Chase’s article on evals emphasizes that without rigorous evaluation, agents remain unreliable. For security, evals must measure accuracy, speed, and adherence to policies.
Step‑by‑Step Guide
- Curate an evaluation dataset – e.g., 100 real security incidents with ground truth labels.
- Use LangChain’s evaluation framework to run the agent against the dataset and compute metrics (precision, recall, F1).
- Automate evals in CI/CD so every change triggers a test suite.
Commands to Set Up LangSmith
Install LangChain and LangSmith pip install langchain langsmith Set environment variables for LangSmith export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY=your_key
Python Evaluation Snippet
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("qa")
result = evaluator.evaluate_strings(
prediction="Malicious IP 203.0.113.5 found",
reference="203.0.113.5 is a known C2 server"
)
print(result)
Explanation
Evals turn agent performance into quantifiable data. By automating them, you catch regressions early and build confidence before deploying to production.
- The Revenge of the Data Scientist: Data-Centric AI for Security
Hamel Husain’s piece highlights that high-quality, domain-specific data often matters more than model choice. For security, this means curating incident reports, threat intel feeds, and system logs.
Step‑by‑Step Guide
- Collect raw security data – e.g., JSON logs from SIEM or EDR.
- Use command-line tools to clean and sample the data.
- Label a subset with tools like Label Studio for supervised fine-tuning.
Linux Data Processing Commands
Extract all unique IP addresses from a log file
grep -oE '[0-9]+.[0-9]+.[0-9]+.[0-9]+' security.log | sort -u > ips.txt
Count occurrences of each event type
awk -F'"' '{print $2}' events.json | sort | uniq -c | sort -nr
Windows PowerShell Equivalent
Get-Content security.log | Select-String -Pattern '\d+.\d+.\d+.\d+' -AllMatches | ForEach-Object { $_.Matches.Value } | Sort-Object -Unique > ips.txt
Explanation
By treating data as a first-class asset, you ensure the agent learns from realistic scenarios rather than generic examples, significantly improving its utility in security operations.
4. Skill Issue: Harness Engineering for Coding Agents
HumanLayer’s article explores how coding agents can be integrated into workflows. In security, these agents can automate repetitive tasks like vulnerability scanning, patch management, or even red team operations.
Step‑by‑Step Guide
- Select an agent framework – e.g., AutoGPT, SuperAGI, or a custom LangChain agent.
- Configure the agent with granular permissions – use read-only API keys or sandboxed execution.
- Set up a feedback channel where the agent can ask for human approval on risky actions.
Example Agent Configuration (YAML)
agent: name: "patch_manager" permissions: - read: ["/var/log", "https://api.nessus.io/scans"] - write: [] - exec: ["/usr/bin/ansible-playbook"] approval_required: - "apply_patch"
Explanation
Harness engineering for coding agents ensures they operate within strict boundaries, preventing accidental damage while still delivering automation benefits.
- Leveraging Codex in an Agent-First World: API Security & Cloud Hardening
OpenAI’s resource on harnessing Codex emphasizes the importance of secure agent interactions. Agents often call external APIs—these endpoints must be hardened to prevent abuse.
Step‑by‑Step Guide
- Implement API rate limiting to prevent agents from overwhelming services.
- Use OAuth 2.0 / JWT for authentication between agents and backend systems.
- Monitor API calls with a centralized logging system to detect anomalies.
API Gateway Configuration (Kong)
Install Kong and add a service curl -i -X POST http://localhost:8001/services/ \ --data name=security-agent \ --data url=http://agent-backend:5000 Add a route with rate limiting curl -i -X POST http://localhost:8001/services/security-agent/routes \ --data paths[]=/agent \ --data plugins=rate-limiting \ --data config.minute=100
Cloud Hardening (AWS IAM)
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::security-logs/"
},
{
"Effect": "Deny",
"Action": "s3:DeleteObject",
"Resource": ""
}
]
}
Explanation
Securing agent APIs and cloud resources is non-negotiable. These steps ensure that even if an agent is compromised, the blast radius is limited.
- The Feedback Loop: Capturing Production Data to Improve Agents
The core insight from Dylan Williams’ post is that production feedback fuels improvement. Capturing what works and what fails allows you to refine both harness and evals.
Step‑by‑Step Guide
- Instrument the agent to log every decision, tool call, and result.
- Stream logs to a centralized observability stack (e.g., ELK, Prometheus).
- Periodically review failure cases to update evals and training data.
Setting Up ELK with Docker
docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:8.6.0 docker run -d --name kibana -p 5601:5601 --link elasticsearch:elasticsearch docker.elastic.co/kibana/kibana:8.6.0
Log Shipping Configuration (Filebeat)
filebeat.inputs: - type: log enabled: true paths: - /var/log/agent/.log output.elasticsearch: hosts: ["localhost:9200"]
Explanation
Without production feedback, you’re flying blind. This loop—define, capture, analyze, improve—is the engine that transforms a prototype into a reliable security tool.
- Real-World Application: Blue Team Agent for Threat Hunting
Let’s combine everything into a practical example: an agent that queries an Elasticsearch SIEM, identifies suspicious IPs, and enriches them with threat intelligence.
Python Snippet (Agent with Harness & Evals)
import os
from elasticsearch import Elasticsearch
from openai import OpenAI
class ThreatHunter:
def <strong>init</strong>(self):
self.es = Elasticsearch(os.getenv("ELASTIC_URL"))
self.openai = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
def query_siem(self, query):
return self.es.search(index="logs-", body={"query": {"query_string": {"query": query}}})
def enrich_ip(self, ip):
Call threat intelligence API (simulated)
return f"Threat data for {ip}: known C2"
def run(self):
Step 1: Get suspicious IPs
results = self.query_siem("event.type: network AND destination.port: 4444")
ips = set(hit["_source"]["destination.ip"] for hit in results["hits"]["hits"])
Step 2: Enrich each IP
reports = [self.enrich_ip(ip) for ip in ips]
Step 3: Generate summary with LLM
summary = self.openai.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": f"Summarize these threats: {reports}"}]
)
return summary.choices[bash].message.content
Run with harness
if <strong>name</strong> == "<strong>main</strong>":
hunter = ThreatHunter()
print(hunter.run())
Explanation
This agent demonstrates a simple but effective security workflow. The harness (environment variables, logging) and evals (compare output against known incidents) would be added to ensure reliability.
What Undercode Say
- Key Takeaway 1: Harness engineering is the practical framework that turns agent potential into production reliability—without it, even the smartest AI will fail in complex security environments.
- Key Takeaway 2: Evaluation is not optional; continuous feedback loops are the only way to improve agent performance, especially when dealing with adversarial inputs and evolving threats.
Analysis
The rush to adopt AI agents often overlooks the foundational work of building robust harnesses and rigorous evals. Yet, as the cited resources show, these are precisely what separate experimental demos from mission‑critical tools. For cybersecurity, where stakes are high, adopting a data‑driven, feedback‑centric approach is essential. The convergence of AI and SecOps will accelerate, but only for those who embrace systematic engineering practices.
Prediction
In the next 12–18 months, we’ll see the rise of specialized “security agent orchestrators” that combine harness engineering, evals, and cloud hardening into integrated platforms. Organizations that invest now in building their own feedback loops will gain a significant advantage, while those relying solely on off‑the‑shelf agents will struggle with reliability and security. The future belongs to teams that treat agent development with the same rigor as they do traditional security engineering.
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Reported By: Dylan Williams – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
