RESEARCH // 01 • AGENTIC SYSTEMS
Agentic AI Systems
We build and investigate intelligent systems that can reason, formulate plans, interact with digital tools, observe runtime outputs, and iteratively converge on complex engineering solutions.
Deterministic Execution Loop
01 — TASK INPUT & CONTEXT PARSING
Autonomous agent ingests natural-language goals, parses ambient environment constraints, and retrieves context from the Model Context Protocol (MCP) host.
// SYSTEM: MCP Context Ingestion
{
"session_id": "swa-ag-892",
"objective": "Design robust state machine for low-power sensor bus",
"context_buffers": ["registers.json", "timing_constraints.sdc"],
"permission_level": "read_write_sandboxed"
}
Agent Architecture
Decoupled control harnesses that isolate the planner from model weights. Uses typed JSON schema contracts to enforce invariant validation.
Tool Execution
Standardized integration with compilers, linters, simulators, and file trees via the Model Context Protocol (MCP) under zero-trust boundaries.
Memory Systems
Hierarchical memory: instantaneous working scratchpads, episodic session buffers, and persistent vector-indexed technical knowledge.
Context Engineering
Adaptive window pruning, KV cache optimizations, and relevance filtering to prevent attention degradation over 200,000+ token trajectories.
Deterministic Planning
Branching Monte Carlo tree search and linear fallback schedules that calculate task success probability prior to executing side-effects.
Feedback Loops
Parsing compiler error diagnostics and runtime stack traces back into intermediate reasoning tokens for rapid autonomous repair.
Automated Evaluation
Deterministic regression suites testing agent convergence speed, token overhead, tool-calling precision, and safety compliance.
Multi-Agent Systems
Role-specialized swarms (Researcher, Architect, Tester, Verifier) coordinating through structured protocols and shared telemetry.