Make agents reliable
in production.
Palma.ai makes MCP tool use dependable across real systems—so agents finish multi-step work correctly, not "almost," and teams can trust outcomes.
Production-grade, not prototype-grade
How Palma.ai increases reliability
Agent-ready capabilities
Palma.ai encourages building and exposing MCP tools as clear, chainable capabilities. Agents succeed more when interfaces are designed for how they actually operate.
Curated toolsets per use case
Reliability improves dramatically when the agent sees the smallest relevant surface. Palma.ai reduces ambiguity by curating what each agent can use.
Safer path for multi-step work
For workflows that are brittle with pure tool-calling, Palma.ai provides a controlled execution path so complex chains run deterministically.
Observability at the tool level
When something fails, know exactly which MCP server, which tool, which step, which policy decision, and what changed from last week.
The problem
Most agent failures aren't model failures—they're tool boundary failures:
Wrong tool selection
Slight schema mistakes that cascade
Multi-hop drift across steps
Partial execution without clear traceability
In the enterprise, "almost correct" is not acceptable—agents must complete workflows end-to-end.
Full visibility
Trace every tool call, end to end
Success Rate
96.8%Tool Calls
4Retries
0Tokens
1,247What "good" looks like
High task completion for real multi-step workflows
Controlled tool surfaces designed for agent chaining
Clear trace of actions + easy root-cause analysis
Reliability that holds as tools and teams grow
Outcomes
Higher task success rates for multi-hop workflows
Fewer "agent flake" incidents in production
Faster debugging and iteration
Confidence to expand automation to more critical systems
Make "agent reliability" a platform capability—not a hope.
Use Cases
What are you solving for?
Different problems, one platform. See how Palma.ai addresses your specific challenges.
Scaling Agents
Pilots work, but enterprise rollout breaks.
One MCP layer for the whole enterprise.
Cost of Agents
Token waste and tool-call loops spike costs.
Predictable cost per completed task.
Agent Reliability
Multi-hop drift and tool boundary failures.
Agents that finish work correctly.