• Posted by: Eng. Donya Bino
• February 16, 2026

Agentic AI vs Agentic AI: Machine Cyberwars

We’ve entered the phase where the most interesting (and dangerous) cyber conflicts are no longer human hacker vs. human defender. They’re increasingly agent vs. agent , autonomous AI systems attacking other autonomous AI systems, defending against them, repairing themselves, adapting in real time, and sometimes even negotiating with each other.

This isn’t science fiction anymore. It’s already happening in fragments in 2025–2026, and the trajectory is very clear.

What “Agentic AI” Really Means Here
An agentic AI is a system that:
1. has persistent goals
2. can plan multi-step actions
3. calls external tools/APIs
4. reasons over long horizons
5. adapts to changing conditions
6. sometimes self-improves or self-repairs

When both the attacker and the defender are agentic, the game changes completely.

Real-World Fragments Already Visible (2025–2026)
1. Red-team agent vs. blue-team agent exercises Companies (and red-team firms) now routinely pit one LLM-based agent swarm against another. Attacker agents chain vulnerabilities (phishing → credential stuffing → API abuse → privilege escalation). Defender agents do live triage: detect anomalous tool calls, kill suspicious sessions, patch on the fly, rotate keys, spin up decoy endpoints. Winner = the agent that survives longest or causes the most damage before being contained.

2. Autonomous malware vs. autonomous EDR Some 2025 ransomware strains already contain lightweight LLM-like reasoning loops (via on-device models or C2-hosted inference). They probe the environment → if EDR agent is detected → try different evasion paths → if still blocked → self-terminate and pivot to fallback C2. Modern EDRs (CrowdStrike, SentinelOne, Microsoft Defender) now run their own agentic reasoning to counter: behavioral scoring + live response agents that quarantine, snapshot memory, and even “talk back” to the malware process.

3. AI-vs-AI phishing & social-engineering wars Attacker agent crafts personalized phishing messages → defender agent (email security product) scores them in real time → attacker agent reads the rejection reason (if leaked) → rewrites the message → loops until it passes. Seen in high-end BEC campaigns targeting crypto and finance.

4. Supply-chain & update wars Attacker agent compromises a software update pipeline → injects malicious payload → defender agent on the endpoint detects anomaly during install → rolls back → attacker agent tries different obfuscation → defender agent adapts detection rules → and so on.

What Makes Machine-vs-Machine Different from Human-vs-Machine
1. Speed → decisions in milliseconds instead of minutes
2. Scale → thousands of parallel agents vs. one tired analyst
3. No emotion/fatigue → no panic selling, no “I’m just going to pay” moment
4. No morals → attacker can try extremely destructive paths without hesitation
5. No sleep → 24/7 escalation loops
6. Adaptive learning → both sides get better with every interaction

The result: fights that look slow and boring from the outside (no flashy screens, no typing montages) but are actually escalating exponentially in milliseconds behind the scenes.

Practical Implications for Organizations Right Now
1. Assume your EDR, SOAR, and XDR are already fighting agentic opponents , tune them for speed, not just accuracy.
2. Build agentic defensive layers (auto-quarantine, auto-rotation of secrets, auto-decoy deployment) before the attackers do.
3. Monitor for “retry storms” , repeated failed API/tool calls from internal systems can be an attacker agent probing your defenses.
4. Limit agent autonomy , give them tight guardrails, human-in-loop on high-risk actions, and hard kill-switches.
5. Treat every 429, 403, 500 spike as a potential adversarial probe.

The quiet war is already machine vs. machine in many environments. The side that builds better agents and better controls around them , will win most of the invisible battles.