The Multi-Agent Future for Investigation and Response Automation

The Shift Toward Agentic AI in Cybersecurity

Agentic AI is reshaping how security operations approach scale, complexity, and speed. Nowhere is this more relevant than in investigation and response.

As cyber threats grow increasingly sophisticated and frequent, security operations centers (SOCs) face mounting pressure to respond swiftly and effectively. Traditionally, cybersecurity teams relied heavily on individual analyst skill and manual, linear processes. Early adoption of AI tools, AI-native solutions provided incremental improvements—automating repetitive tasks and offering quick summarization—but often fell short when complexity increased. The industry is now poised at the edge of a transformative shift: embracing Multi-Agent Systems (MAS) to fundamentally enhance investigative capabilities.

Understanding Multi-Agent Systems (MAS)

At its core, a Multi-Agent System (a practical application of agentic AI) is a coordinated network of specialized AI agents, each designed to handle a distinct area of expertise, collaboratively working to solve complex problems. Think of it as an expertly choreographed team, each member possessing unique specialized skills who communicate and collaborate continuously to achieve a common investigative goal.

Here’s how a typical MAS works:

• Context capture: As soon as an investigation begins, the system gathers relevant data—case history, evidence collected, prior analyst inputs.

• Agent selection: The orchestrator determines which agents are needed to fulfill a given task. For example, a detection agent might write triage rules, while a scripting agent prepares containment actions.

• Parallel tasking: Agents operate simultaneously and collaboratively. Each executes its function while remaining aware of shared context.

• Output synthesis: An orchestrator merges responses, resolves contradictions, and delivers a coherent, streamlined outcome to the analyst.

This is more than just automation—it’s a dynamic, adaptive system designed to think and act like an investigative team.

Why MAS Is a Perfect Fit for Investigation & Response 

Security investigations rarely follow a straight line. They require diverse skill sets, fluid collaboration, and iterative exploration of evidence. MAS is a natural fit because it mirrors the way human-led investigations already unfold, only faster, more consistent, and always available. More importantly, MAS is purpose-built to support the full spectrum of investigation and response workflows—from initial triage and enrichment to deep forensic analysis and containment planning.

Here’s why MAS aligns so well:

1. Multiple disciplines, one team: MAS reflects the real-world structure of a SOC. One agent investigates process lineage, another writes detection rules, while another prepares remediation workflows. Each agent has deep specialization.

2. Parallel workflows: Tasks like IoC enrichment, memory analysis, and timeline correlation can happen at once—eliminating bottlenecks and accelerating response.

3. Shared, persistent context: MAS agents operate with full visibility into the investigation timeline, previously collected evidence, and analyst prompts—avoiding repetition and reducing errors.

4. Seamless hand-offs: With orchestration at the core, each agent hands off relevant findings to the next without losing context. Analysts stay in the loop, but the machine does the heavy lifting.

5. Coherent outcomes: Agentic AI systems like MAS don’t leave analysts to piece together fragmented outputs—they streamline complexity into usable insight. The orchestrator integrates and rationalizes multiple agent outputs into a clear answer or next step.

MAS vs. Single-Agent AI

While single-agent chatbots can be helpful for simple tasks—like summarizing an alert or suggesting next steps—they fall short when applied to complex investigations. These tools operate in a vacuum, handling one query at a time, with no awareness of investigative history or broader context.

By contrast, MAS systems:

• Coordinate multiple tasks simultaneously, rather than handling one prompt at a time.

• Specialize deeply, allowing agents to perform more advanced reasoning within their domain.

• Retain and build context, enabling investigations to evolve over time rather than reset with each new input.

• Deliver comprehensive responses, combining inputs from multiple experts rather than relying on a single model’s general knowledge.

In short: where single-agent chatbots offer reactive support, MAS delivers proactive, collaborative investigation assistance that mirrors the way analysts already think and work.

Fueling Agentic AI: The Data

For any form of agentic AI to be effective, whether orchestrating a multi-agent investigation or answering a single contextual query, the foundation is always the same: data. The quality, specificity, and completeness of that data directly impacts how well the system performs.

This matters now more than ever. As interest in AI-SOC platforms grows, many emerging players focus heavily on the "engine"—the language model or orchestration layer—while overlooking the critical role of the underlying data. Without sufficient depth, AI risks offering superficial results that lack investigative value.

To support meaningful investigation and response workflows, agentic AI needs access to context-rich, ground-truth evidence—what was happening in memory, on disk, and across timelines at the moment an incident occurred.

This isn’t just a nice-to-have. It's what transforms an AI recommendation from a guess into a grounded hypothesis. When data is detailed, real-time, and tied to investigative timelines, AI agents can:

• Operate with confidence and precision

• Tailor outputs to your unique environment

• Build institutional knowledge that compounds over time

Fleet AI: Multi-Agent Intelligence, Built for Investigation

At Binalyze, our mission has always been to empower defenders, with the visibility, context, and confidence to drive investigations forward. Fleet AI advances this mission by embedding a new layer of expertise directly into Binalyze AIR: a multi-agent system (MAS) purpose-built for investigation and response.

Our vision is grounded in three core principles:

• Embedded, not bolted on: Fleet agents are tightly integrated into AIR’s workflows — working with forensic data and case context from the start, not sitting on the sidelines.

• Experts, not just copilots: Each agent is domain-specialized — detection engineering, scripting, triage planning — with more to come, including reverse engineering, reporting, and containment.

• Human-in-the-loop by design: Analysts stay in full control. Every output is assistive, every action analyst-approved.

Roadmap in Motion

• Live now: The foundation of our MAS architecture is here — with our first Detection Engineer agent live for natural language rule generation (Sigma, YARA, osquery).

• Next: A Scripting agent, support for custom LLMs (BYO-AI), and further expansion of skill-specific agents.

• Coming soon: Investigation memory and cross-agent collaboration powered by the Model Context Protocol (MCP) — enabling shared context and dynamic orchestration.

• Future: Semi-autonomous workflows, proactive investigations, and full case-aware automation.

Fleet AI extends the power of Binalyze AIR by surrounding them with a team  of skill-set specific AI forensic-aware teammates that help move faster, with clarity and confidence, and stay in control.

This is where investigation goes next. And it’s already underway.

Explore Fleet AI capabilities.