Palma

Product Introduction

1. Definition: Palma is an enterprise-grade governance, optimization, and control platform specifically designed for AI agents utilizing the Model Context Protocol (MCP). It functions as a centralized gateway and management layer that sits between AI agent frameworks (such as Claude Code, Cursor, or autonomous agent fleets) and core enterprise systems (like Jira, GitHub, PostgreSQL, and internal APIs).

2. Core Value Proposition: Palma exists to solve the "security versus speed" dilemma in agentic AI deployment. By providing a secure MCP gateway, Palma allows organizations to unlock the full potential of AI agents while maintaining strict control over data access, enforcing per-tool policies, and auditing every execution. It enables the scalable adoption of Agentic AI by providing the necessary infrastructure for compliance, cost management, and operational reliability.

Main Features

1. Per-Tool Policy and Human-in-the-Loop (HITL) Approvals: Palma provides granular control over AI agent capabilities by allowing administrators to set specific policies for individual tool calls. Instead of granting broad system access, organizations can enforce "Human-in-the-Loop" requirements for high-risk actions (e.g., merging code or deleting database records) while allowing low-risk actions to execute autonomously. This governance layer ensures that agent behavior aligns with corporate security protocols.

2. Context Optimization and Token Pruning: To improve agent accuracy and reduce operational costs, Palma features an optimization engine that prunes and scopes context before execution. By filtering out irrelevant data and optimizing the information sent to the Large Language Model (LLM), Palma reduces token consumption and prevents the "lost in the middle" phenomenon, ensuring the agent has the exact data needed for a successful tool call.

3. Comprehensive Auditability and Cost Tracking: Palma serves as a centralized telemetry hub for agentic workflows. It records every tool call, attributing token usage and costs to specific agents, departments, or workflows. This provides deep observability into AI performance and ROI. The platform also triggers anomaly alerts when it detects unusual spending patterns or suspicious tool usage, meeting enterprise requirements for FinOps and security monitoring.

4. 19,000+ Out-of-the-Box MCP Server Support: As a founding member of the Agentic AI Foundation, Palma offers native interoperability with a vast ecosystem of over 19,000 MCP servers. This allows teams to connect agents to tools like Okta, MongoDB, Stripe, Slack, and Figma instantly using an open standard, preventing vendor lock-in and ensuring future-proof integration.

Problems Solved

1. Security Risks of Autonomous Agents: Without a governance layer, AI agents often operate with over-privileged access, creating a "Shadow AI" environment where tool calls are unmonitored. Palma addresses this by acting as a zero-trust gateway, ensuring no agent can bypass established governance frameworks.

2. Target Audience:

• Agent Builders: Developers who need reliable, secure access to enterprise data to build functional agents.
• MCP Developers: Engineers designing MCP servers who want to focus on capability rather than rebuilding security and routing logic.
• Governance and Compliance Teams: CISO and IT leaders who must ensure AI usage complies with the EU AI Act, DORA, and NIST/ISO standards.
• Enterprise Architects: Professionals responsible for creating a stable, scalable AI infrastructure that doesn't require constant rebuilding as LLM models change.

3. Use Cases:

• Enterprise MCP Gateway: Providing a single entry point for all AI interactions with internal legacy systems and SaaS tools.
• Developer Productivity: Enabling secure use of tools like Claude Code or Cursor with enterprise-wide repositories without leaking sensitive source code.
• Autonomous Agent Fleets: Managing the execution and monitoring of hundreds of agents performing background tasks without direct human supervision.

Unique Advantages

1. Future-Proof AI Architecture: Unlike custom integrations that break when a model or framework is updated, Palma puts a stable MCP layer in front of core systems. This separation of concerns allows companies to swap LLM providers or agent frameworks without rebuilding their entire integration stack.

2. Founding Member of the Agentic AI Foundation: Palma is not just a consumer of the Model Context Protocol but a key stakeholder in its evolution. This ensures the platform remains at the cutting edge of open standards and natively supports the latest advancements in agentic communication.

3. Flexible Deployment Models: To meet the strict data residency requirements of highly regulated industries (finance, healthcare, government), Palma offers deployment options including SaaS, On-Premise, and Virtual Private Cloud (VPC), ensuring that sensitive data never leaves the controlled environment.

Frequently Asked Questions (FAQ)

1. What is the difference between MCP and a standard CLI for AI agents? While Command Line Interfaces (CLI) are powerful for local developer tasks, they lack the governance, auditability, and multi-user scaling required by enterprises. MCP, managed through a platform like Palma, provides a standardized, secure way for agents to interact with diverse systems with centralized policy enforcement and observability that CLIs cannot provide.

2. How does Palma.ai reduce the cost of running AI agents? Palma reduces costs through context pruning and token optimization. By stripping unnecessary metadata and scoping the context to only what is required for the specific tool call, Palma minimizes the number of tokens processed by the LLM. Additionally, its FinOps tracking identifies inefficient agents or "looping" behaviors that would otherwise lead to unexpected expenses.

3. Can Palma.ai help with compliance for the EU AI Act? Yes. Palma provides the necessary audit logs, transparency reports, and human-in-the-loop controls required by frameworks like the EU AI Act, DORA, and NIST. It ensures that every AI action is accountable, traceable, and subject to organizational guardrails.

4. Does Palma support both human-triggered and autonomous agents? Yes. Palma is designed for both "On behalf of Humans" scenarios (where developers use MCP-enabled IDEs like VSCode or Cursor) and "Autonomous Agents" (where agent fleets perform tasks independently). In both cases, Palma applies the same rigorous governance and security checks.