SuperOptiX vs Agent Bricks
DSPy-Powered Titans of Agentic AI
We're entering a golden era for agent frameworks, where building powerful, production-grade AI agents no longer requires months of custom engineering. Two DSPy-powered frameworks have recently emerged as frontrunners in this space.
We're entering a golden era for agent frameworks, where building powerful, production-grade AI agents no longer requires months of custom engineering. There are many frameworks available in the market which are based on prompt tinkering techniques. DSPy is the only framework which allows developers to focus on programming rather prompting.
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The first DSPy powered Agentic AI framework was released by DataBricks called Agent Bricks this year in June. Then on July 17th Superagentic AI released the SuperOptix which follows TDD/BDD for Agentic AI. Two DSPy-powered frameworks have recently emerged as frontrunners in this space: SuperOptiX by Superagentic AI, and Agent Bricks by Databricks. Both share the same underlying DNA, they leverage DSPy, the compositional optimization framework, yet their philosophies could not be more different.
The Contenders
Agent Bricks
by Databricks
Unveiled in June 2025, Agent Bricks is designed for speed, simplicity, and automation, focusing on letting enterprise users spin up agents with minimal effort.
SuperOptiX
by Superagentic AI
Released on July 17th, 2025, SuperOptiX puts engineers firmly in the driver's seat, offering full control over agent specification, evaluation, orchestration, memory, and observability.
Feature Comparison
| Feature | SuperOptiX | Agent Bricks |
|---|---|---|
| Philosophy | Engineering discipline | Automated magic |
| Task Definition | Structured DSL | Natural language |
| Evaluation | Transparent process | Auto-evaluation |
| Optimization | Manual strategies | TAO |
| Orchestration | Advanced workflows | Basic orchestration |
| Memory | Memory stack | Minimal memory |
Philosophical Foundations: Engineering Discipline vs Automated Magic
SuperOptiX: Engineering Discipline
SuperOptiX was built for developers and AI engineers who want full transparency and control. It takes a "spec-first" approach where agents are defined declaratively using a DSL called SuperSpec, tested with BDD-style scenarios, and orchestrated in pipelines.
- Engineering-centric design
- Explicit specifications
- Built-in CI/CD readiness
Agent Bricks: Automated Magic
Agent Bricks adopts a "no-code, auto-magic" approach. The idea is simple: you describe your task in natural language, and the system builds, optimizes, and evaluates your agent behind the scenes.
- Designed for business outcomes
- Auto-evaluation and optimization
- Deep MLflow integration
Task Definition: Natural Language vs Structured DSL
Agent Bricks: Natural Language
"Build an agent that can analyze customer feedback and generate actionable insights for product improvement"
SuperOptiX: Structured DSL
apiVersion: agent/v1
kind: AgentSpec
metadata:
name: "customer_feedback_analyzer"
tier: "genies"
version: "1.0.0"
spec:
persona:
role: "Customer Feedback Analyst"
goal: "Analyze customer feedback and generate actionable insights"
tasks:
- name: "analyze_feedback"
instruction: "Analyze customer feedback for sentiment, themes, and actionable insights"
inputs: [{"name": "feedback", "type": "str"}]
outputs: [{"name": "analysis", "type": "str"}]Evaluation: Black Box vs Transparent
Agent Bricks: Auto-Evaluation
Agent Bricks features automatic evaluation. It generates its own benchmarks and evaluation functions, giving users instant feedback on performance.
SuperOptiX: Transparent Process
SuperOptiX provides full control via CLI and DSL with BDD-style evaluation scenarios.
# Run BDD-style evaluation super agent evaluate customer_analyzer --verbose # Output: # Functional accuracy: 95% # Behavioral compliance: 92% # Safety checks: All passed # BDD scenarios passed: 80%
Optimization: TAO vs Manual Strategy
Agent Bricks: TAO
Agent Bricks leverages TAO (Test-time Adaptive Optimization), a cutting-edge technique that uses unlabeled data to optimize agents post-deployment.
SuperOptiX: Manual Strategies
SuperOptiX offers multiple explicit strategies with transparent optimization paths.
# Manual optimization super agent optimize customer_analyzer super agent evaluate customer_analyzer # Available optimizers: # - BootstrapFewShot # - KNNFewShot # - LabeledFewShot
Memory & Knowledge: Minimal vs Multi-layered
Agent Bricks: Minimal Memory
Agent Bricks offers basic vector memory, but lacks detail around long-term storage, session memory, or episodic learning.
SuperOptiX: Memory Stack
SuperOptiX includes a full memory stack with multiple layers and backend support.
Final Verdict: Choose the Titan That Matches Your Mindset
Agent Bricks Winner
Agent Bricks is the clear winner for teams that want fast deployment and automated tuning without engineering overhead. It's ideal if you're deeply integrated with Databricks, want rapid iteration, and trust automation to handle the messy details.
SuperOptiX Winner
SuperOptiX is for those who treat AI agents as systems, who want to test, optimize, and ship with confidence. It's for developers who want version control, orchestration, memory control, and long-term maintainability.
Important Note
Databricks is a big company with huge resources while Superagentic AI is just a 2-month-old startup. There is no head-to-head comparison at this stage, so Agent Bricks might be the right choice for most users if you're in the Databricks world. However, SuperOptiX offers an alternative if you want to do Context Engineering and let SuperOptiX handle DSPy Agent Engineering.
Ready to Choose Your Framework?
Explore SuperOptiX and see how engineering discipline can transform your AI agent development.
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