Competitive Analysis: ABS Core vs Current Alternatives

The Core Thesis

The AI industry is transitioning from "Generative AI" (text output) to "Agentic AI" (taking actions). Existing security paradigms are built for the former resulting in solutions that are probabilistic, slow, and reactive. ABS Core is deterministic, fast, and proactive (Runtime Enforcement).

Competitor Categories

1. LLM Firewalls (Lakera, PromptArmor, AWS Bedrock Guardrails)

• How they work: Analyze the input prompt and output completion using smaller, faster ML models to detect injections, toxicity, or PII.
• The Problem: They are probabilistic. They fail against zero-day prompt injections. More importantly, they protect the prompt, not the action. If an agent uses a tool delete_user(), a prompt firewall doesn't know the difference between a legitimate or malicious intent once it reaches the execution layer.
• ABS Core Advantage: Deterministic governance. We don't guess if a prompt is malicious; we evaluate if the resulting action (tool call / API request) violates a hardcoded security policy.

2. General API Gateways (Kong, Apigee, Cloudflare API Shield)

• How they work: Rate limiting, authentication, WAF rules.
• The Problem: They are blind to application semantic intent. An API gateway sees a valid JWT and lets the request pass. It cannot understand "Agent A is allowed to query order status but NOT issue a refund."
• ABS Core Advantage: Deep Contextual Inspection. ABS Core evaluates the payload of the tool call against the identity of the agent and the current state context.

3. Agent Framework Internal Controls (LangChain, LlamaIndex, CrewAI)

• How they work: Built-in "human-in-the-loop" callbacks or basic allow-lists.
• The Problem: The security is coupled to the application logic. If the agent code is bypassed or compromised, the security falls. It also creates vendor lock-in.
• ABS Core Advantage: Infrastructure-level isolation. ABS Core sits outside the agent framework as a separate enforcement layer, creating a true security boundary.

Win Criteria (Why we win)

1. Sub-40ms Deterministic Enforcement: Fast enough to sit in the runtime path without destroying UX.
2. Agnostic Architecture: Works with any framework (CrewAI, raw Python) and any model.
3. Cryptographic Auditability: Every decision generates an immutable ledger entry (Hash), essential for enterprise compliance/regulators.