Three Capabilities LLMs Lack
LLMs generate text. Masar understands structure.
Planning
Masar knows 93 behavioral patterns across 18 domains. Given a partial result and a goal, it produces dependency-ordered instructions with exact parameters. The LLM does not guess what to build. Masar tells it.
Verification
Predicts validity in under 5ms without running the compiler. Catches 20 categories of structural errors. When something fails, beam search finds the optimal repair sequence entirely in latent space.
Memory
Stores agent experiences as structured episodes. Clusters similar episodes into reusable patterns over time. Your agent builds expertise from its own work, without retraining.
How Masar Works
Embed
Masar converts the current state into a compact representation that captures structural topology. Similar problems land in the same region of the representation space.
Match
Finds the closest known pattern from the library. 93 behaviors across e-commerce, healthcare, CRM, project management, games, and 12 more domains.
Plan
Computes the structural difference between where you are and where you need to be. Decomposes it into 7 dependency levels of parameterized instructions.
Verify
After each step, predicts validity and error categories. Catches problems before the LLM compounds them. When errors occur, beam search finds the shortest repair path.
Remember
Stores the completed result as an episode. Over time, episodes cluster into new patterns. The 93 hand-authored behaviors are just the seed. The library grows from experience.
By the Numbers
< 5ms
Planning latency
94%
Validity prediction accuracy
93
Built-in behavioral patterns
18
Domains covered
20
Error categories detected
6
API endpoints
Thinking, Fast and Slow
Daniel Kahneman's research showed that human cognition has two systems. System 1 is fast, intuitive, and associative. System 2 is slow, deliberate, and structural. You need both to make good decisions.
LLMs are System 1. They respond instantly, pattern-match from training data, and generate fluent text. But they cannot plan multi-step processes, verify structural correctness, or learn from their own experience. That is System 2.
Masar is System 2 for any LLM agent. It handles the deliberate, structural thinking that language models cannot do. The LLM handles language and generation. Masar handles planning, verification, and memory. Together, they form a complete agent.