Introducing UnieAI Agent Core
Agent Core decides when the model reasons, which tools it calls via MCP, what it remembers, and runs it safely in a sandbox.
Baseline scores from the public leaderboard; the UnieAI bar is our internal result.
GPT-5.2 (xhigh)
MiniMax-M2 × UnieAI Agent Core 2
GPT-5.2 (medium)
gpt-oss-120b (high)
gpt-oss-20B (high)
Nova 2.0 Pro
Claude 4.5 Haiku
MiniMax-M2 (baseline)
A purpose-built harness makes models stronger and more reliable. Agent Core 2 lifts MiniMax-M2 on AIME from 78.3% to 97.2%.
AIME 2025 uplift on MiniMax-M2 (78.3% → 97.2%) with Agent Core 2
Concurrent agent turns per replica vs. ~10–40 for sandbox-per-agent*
Memory per agent turn — ~0 CPU while waiting on model & tools*
Cold start — stateless, linear horizontal scaling
converging
Two halves of one agent-inference engine.
UnieAI Agent Core
Decoupled, async agent runtime — hundreds of concurrent turns per process, single-digit MB each.
UnieInfra
Token-efficient throughput density and low TTFT for agent inference. Converging with Agent Core into one engine.
One harness — smarter models, cheaper turns.
Decides when the model reasons, calls tools, and how a task is decomposed.
Bash, file edit, web & KB search, and any MCP server as a pluggable tool source.
Run code and tools safely — decoupled from the agent loop, not a VM per agent.
A resumable timeline ledger persists every turn for replay and observability.
Tree-based retrieval grounds answers in your knowledge base, with sources.
I/O-bound turns are multiplexed — a waiting turn uses ~0 CPU, only its context memory.
intelligence
The same open model gets materially stronger and more reliable when it runs inside a purpose-built harness — better planning, better tool use, stable loops.
economics
Traditional frameworks give every agent its own sandbox: hundreds of MB to GB each, seconds to cold-start, so high concurrency means spinning up hundreds of VMs. Agent Core decouples the agent into efficient services and multiplexes I/O-bound turns in a single process.
Build agents on a harness you can trust.