Our launch post introduced Cyware AI and the philosophy behind it: purpose-built agents that plan, execute, validate, and adapt across real security workflows, right inside the tools analysts already use. This post goes deeper. Below is a detailed look at every agent currently available in the Cyware AI Agent Hub, what each one does, and the specific operational pain it eliminates.

The Agent Hub

Cyware AI is delivered through the Agent Hub, a workbench available both natively inside Cyware Intel Exchange as an in-product floater and as a browser extension for Chrome and Edge. Analysts access agents without leaving their current workflow. The agents connect back to your Cyware deployment via API, with no local data storage, full audit logging, and tenant isolation throughout.

The current set of agents covers the most time-intensive steps in CTI and SOC operations. New agents are being added continuously as the platform expands. Here is every agent available today.

Threat Intelligence Agent

The amount of time a CTI analyst spends before a single finding is documented is staggering. A new threat advisory arrives. The analyst reads it, manually extracts IOCs, switches to an enrichment tool, cross-references the actor against past reports, chases aliases across vendor nomenclature, assesses relevancy to the organization's environment, and then begins writing. That process, repeated across every feed and advisory in the queue, is where analyst hours disappear.

The Threat Intelligence Agent is designed to eliminate the extraction and enrichment loop entirely. It reads threat reports, feeds, and advisories the moment they land, pulls out indicators and actor context automatically, profiles threat actors and malware families, suggests relationship links, and assesses environmental relevancy against your specific deployment. The analyst receives a structured, enriched intelligence object ready for review rather than a raw report waiting to be processed. CTI workflows that previously took hours compress to minutes without any reduction in analytical depth.

Detection Engineering Agent

The gap between knowing about a threat and being able to detect it is wide, and it is usually measured in manual hours. Converting a threat report into a production-ready YARA rule, a Sigma detection, or a validated SIEM query requires specialized engineering depth that most security teams cannot sustain at the pace intelligence arrives. Rules get written without testing. False positives erode analyst trust in detections. And the window between a published advisory and an active detection stays open longer than it should.

The Detection Engineering Agent bridges that gap automatically. It analyzes the full threat context, including IOCs, TTPs, and malware behavior, and generates production-ready Threat Defender Library rules and SIEM queries. What makes it different from a rule generator is the validation step: it converts detection logic into Splunk SPL and runs it against live telemetry before the rule is ever published. Analysts know whether their detection works before it goes out. Approved rules can then be pushed instantly to all member organizations through the TDL integration, closing the intelligence-to-detection cycle at scale.

Attack Flow Agent

A list of indicators tells you what was found at a crime scene. It does not tell you the sequence of events that led there. Reconstructing an attack timeline from forensic logs, incident reports, and TTP data is among the most labor-intensive tasks an analyst faces, and it is also the work that most directly enables proactive defense. Without a behavioral sequence, defenders cannot identify where a kill chain could have been interrupted, cannot anticipate the next step an attacker is likely to take, and cannot build emulation scenarios that reflect how adversaries actually operate.

The Attack Flow Agent ingests incident logs, malware analysis reports, and threat actor TTP data, and generates machine-readable adversary behavior sequences built on the MITRE Attack Flow standard in STIX 2.1. Each step is mapped to ATT&CK tactics and techniques. The result is not just a visualization but actionable intelligence: the agent can identify the first steps of a known flow and suggest what comes next, overlay reconstructed flows onto existing security controls to find defensive gaps, and generate emulation blueprints for red team exercises. Static indicator lists become dynamic behavioral models.

AI-Powered Playbook Builder

Building automation workflows in a SOC has always been gated by the same bottleneck: someone technical has to be in the room. Setting up a new playbook means understanding the orchestration layer, knowing which integrations are available, and translating an analyst's plain-language idea into a structured workflow that actually executes correctly. That dependency on technical users slows down automation adoption and leaves most playbook ideas sitting in a backlog waiting for developer time that never arrives.

The AI-Powered Playbook Builder removes that gate entirely. An analyst describes what they need in plain language, and the builder generates a complete, ready-to-run automation workflow using Cyware Orchestrate's integration library. A response workflow that once required a developer to configure from scratch gets produced in minutes from a single prompt, then customized as needed. Teams that were bottlenecked on playbook creation now have a direct path from operational idea to deployed automation, without any coding required.

AI-Powered Playbook Runlog Debugger

Playbook failures are inevitable in any automation-heavy environment. When a playbook breaks mid-execution, diagnosing the cause has traditionally meant scrolling through dense execution logs, cross-referencing integration documentation, and applying enough technical knowledge to distinguish a configuration error from an API timeout from a logic fault. For analysts who are not developers, that process is slow, frustrating, and often requires escalating to someone with deeper technical expertise, introducing delays at exactly the moment response speed matters most.

The AI-Powered Runlog Debugger is embedded directly in the runlog interface, where failures are first encountered. When an execution fails, the debugger automatically analyzes what went wrong, identifies the exact point and cause of failure, and provides step-by-step guidance to resolve it. An analyst does not need to interpret raw logs or understand the underlying orchestration architecture. They see what broke, why it broke, and what to do about it, in plain language, without ever leaving the interface where the failure appeared. Troubleshooting that previously required escalation now gets resolved in the same workflow where the problem surfaced.

Contextual Intelligence Agent

A threat intelligence platform accumulates enormous volumes of data over time. The problem most teams hit is not volume but legibility. Technical data without narrative forces analysts to manually piece together context from multiple objects before anything actionable emerges. An IP sits in the platform with no summary. A malware report is stored but never tagged. A relationship between a new indicator and a known campaign goes unnoticed because no one had time to look.

The Contextual Intelligence Agent acts as the cognitive layer sitting above the raw data. It automatically generates technical and executive summaries for any threat object so analysts understand the full scope in seconds rather than minutes. It proactively suggests tags, metadata, and cross-entity relationships based on content analysis, including reasoned connections: not just 'this IP may relate to APT28' but 'I linked this IP to APT28 because of shared JA3 fingerprints.' The TIP stops being a data warehouse and starts operating as an active intelligence resource.

Alias Consolidation Agent

APT28 is also Fancy Bear. Also Strontium. Also Sofacy. Also Pawn Storm. A single threat actor can carry a dozen vendor-assigned names, and in most TIPs, each name becomes a separate profile. The consequences are concrete: risk scores reflect a fraction of the actor's true sighting count, detection rules miss known threats because they reference only one alias variant, and analysts spend hours manually cross-referencing industry reports just to confirm two profiles are the same entity.

The Alias Consolidation Agent resolves this through a combination of semantic similarity analysis, TTP fingerprinting, and historical overlap detection. When it identifies likely duplicates, it surfaces them for analyst review with a confidence-weighted rationale. Upon approval, it merges the relationship graphs of the linked profiles, unifying everything the platform knows about that entity into a single record. The downstream effects compound positively: scoring becomes accurate, detection rules become comprehensive, and every other agent in the platform works with cleaner data.

Tag Grouping Agent

Inconsistent tagging degrades a TIP quietly and progressively. New teams face a blank canvas with no tagging structure, leading to ad-hoc labels that fragment intelligence and make the platform hard to query. Scaling teams find their tag groups going stale as new actor aliases and campaign variants emerge without being captured. And for both, surfacing specific intelligence often requires mastering a query syntax that not every analyst has time to learn.

The Tag Grouping Agent automates the creation and ongoing maintenance of the intelligence taxonomy. It suggests an industry-relevant starter set of tags and tag groups based on your sector and regional profile, keeps those groups current by monitoring for new aliases and campaign tags, and translates natural language requests directly into Cyware Query Language queries. An analyst who wants to surface all high-risk malware targeting the energy sector in Europe types the request in plain English and gets a structured, executable query back. The TIP becomes navigable for every skill level on the team.

AI-Powered Custom Code Generator

Automation playbooks frequently need custom logic that off-the-shelf actions cannot cover. Parsing an unusual log format, transforming API output into a specific structure, building conditional logic that accounts for environment-specific behavior: these tasks typically require a Python developer, which most security teams do not have embedded in their operations. The result is either a workaround that is less precise than the team needs, or a ticket that waits for engineering resources that are stretched across the entire organization.

The AI-Powered Custom Code Generator solves this directly inside the playbook canvas. An analyst describes the logic they need in plain language, and the generator produces a working Python code block ready to drop into the workflow. There is no context-switching to a development environment, no waiting on a developer, and no requirement for the analyst to write or debug code themselves. Custom logic that previously required specialized skills becomes accessible to the entire team, and the accuracy and consistency of automation logic improves across every skill level.

SOC Analysis Agent

Alert triage is the most repetitive work in a SOC. For every alert worth investigating, an analyst typically needs to pull context from the SIEM, check EDR telemetry, query threat intel, review past incidents, and consult enrichment sources before they can form a hypothesis. Each of those steps is a context switch. Each context switch is time lost. Across a high-volume SOC, that adds up to thousands of hours a year spent on mechanics rather than analysis.

The SOC Analysis Agent handles the context-gathering automatically. When an analyst opens an incident, the agent identifies the affected assets, enriches every indicator in scope, connects signals across SIEMs, EDRs, and ticketing systems that would otherwise be reviewed in isolation, and surfaces prioritized next steps. The analyst walks into the investigation already oriented. What was previously a forty-five minute process of assembling context becomes the starting point rather than the work itself.

Incident Reporting Agent

After an incident is resolved, the documentation begins. A senior analyst sits down to translate hours of technical investigation into a structured report that communicates clearly to security leadership, legal teams, compliance stakeholders, and technical reviewers simultaneously. The challenge is not just speed but calibration: the same findings need to land differently for different audiences, and that calibration is typically done manually, under pressure, by someone who would rather be on the next threat.

The Incident Reporting Agent generates structured, audience-calibrated incident reports directly from the incident context inside Cyware Respond. Executive summaries, technical post-incident reviews, and stakeholder communications are produced as drafts ready for analyst review, not blank pages. Reporting becomes consistent across the team regardless of who handled the investigation, and the senior analyst's time stays where it belongs.

What Comes Next

The agents above represent the current release. The Agent Hub is designed to expand continuously as new agents are built and released. Several are already in development, including agents for AI-generated detection rules tied directly to incoming alerts, advanced attack flow orchestration with third-party action integration, AI-driven tag suggestion at the object level, and a next-generation alias detection and normalization agent operating across the full SDO corpus.

This is not a fixed product. The Agent Hub grows with the platform, and each new agent is built to the same standard: purpose-built for a specific operational pain, auditable, and designed to keep the analyst in control of every consequential decision.

Cyware AI is available now. If you are a Cyware customer, speak with your account team about Agent Hub access. To explore the full platform, visit cyware.com/ai or request a demo.