AgentLock VS Tragentics

AgentLock's answer

TypeScript across the stack: a React dashboard on Vercel, Supabase with Postgres, Auth and Edge Functions for data, policies and the encrypted vault, a self-hosted runner for actions that need a private network, React Native for the iOS and Android approval app, and Stripe for billing.

Tragentics's answer:

TypeScript end to end. The application is built on Next.js and React; data lives in PostgreSQL with row-level security; the platform runs on AWS. Security primitives: AES-256-GCM encryption for credentials and endpoint URLs at rest, Ed25519 signatures for agent identity, HMAC-SHA256 webhook signing, and TOTP multi-factor authentication for accounts.

AgentLock's answer

AgentLock sits between the agent and the outside world instead of trusting the agent to behave. Every HTTP call, MCP tool call, browser action or SSH command becomes a request that a policy decides on, and anything sensitive waits for a human approval that arrives on the phone. Credentials live in the vault and are injected only at execution time, so an agent can use a key without ever seeing it - and if nobody approves in time, the action simply does not happen.

Tragentics's answer:

AI agent security has two planes: the behavior plane (what an agent decides and says) and the infrastructure plane (what an agent is and touches — identity, credentials, transport, audit). Most vendors sell one point on that map. Tragentics covers the infrastructure plane as one integrated layer: it authenticates every agent, injects keys from an encrypted Credential Vault so agents never hold them, routes every call through a content-blind relay, and records a metadata-only audit trail — across platforms and protocols. And it is content-blind by design: it never reads, stores, or executes what your agents say, so it can never become a second copy of your data.

AgentLock's answer

A secrets manager stores keys, but once an agent holds one, nothing stops it from making the wrong call. AgentLock covers both halves: the encrypted vault plus a gateway with policies, human approval, automatic redaction, a full timeline and undo for supported actions. It is available as an MCP server and an SDK, so an existing agent can be routed through it without a rewrite, and the free plan already covers 100 actions a month with one agent.

Tragentics's answer:

Honestly, it depends on what you're comparing. Guardrail platforms (Zenity, Straiker, Obsidian) secure the behavior plane — prompt injection, runtime monitoring — and they're complementary rather than competing; many teams want both planes covered. Against infrastructure point tools, the difference is integration and deployment: secrets managers like Infisical, Akeyless, and HashiCorp Vault give you a vault you run and wire up yourself, and identity tools like Aembit give you workload identity — Tragentics gives you identity, the Credential Vault, content-blind transport, and a metadata-only audit trail as one hosted layer with nothing to deploy. Keys are injected by the relay at call time, so the calling agent never holds them, and it works with any external API your agents call — including third-party SaaS you don't control.

AgentLock's answer

Developers who run autonomous agents against real systems - deployments, payments, mail, customer data - and want an audit trail and a stop button instead of hoping the prompt holds. Small teams also use it to let an agent work with shared credentials without handing the keys to every team member or to the model itself.

Tragentics's answer:

Teams running AI agents that call real APIs — from a solo developer with two agents to an enterprise fleet. The typical user is a developer, platform engineer, or security engineer wiring agents together (MCP, A2A, ACP, OpenAI-compatible, ANP) who doesn't want to build identity, secrets handling, and audit infrastructure from scratch. Tragentics secures the agents you already own, wherever they run; organizations additionally get multi-tenant orgs, roles, and permission scopes.

Tragentics's answer:

Tragentics launched in 2026, built around one observation: AI agents were multiplying faster than the security around them. Almost everyone was securing what agents say — prompt-injection filters, guardrails, output monitoring — while the things agents are and touch (identities, API keys, connections, audit trails) were handled with copy-pasted keys and hope. Tragentics was built to be that missing infrastructure layer, with one principle locked in from day one: the security layer itself must be content-blind. A tool that reads your agents' traffic in order to protect it becomes a second copy of your most sensitive data — so Tragentics never reads, stores, or executes what it routes.