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.
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.
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 | 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 |
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.
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.
"Build an agent that can analyze customer feedback and generate actionable insights for product improvement"
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"}]Agent Bricks features automatic evaluation. It generates its own benchmarks and evaluation functions, giving users instant feedback on performance.
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%
Agent Bricks leverages TAO (Test-time Adaptive Optimization), a cutting-edge technique that uses unlabeled data to optimize agents post-deployment.
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
Agent Bricks offers basic vector memory, but lacks detail around long-term storage, session memory, or episodic learning.
SuperOptiX includes a full memory stack with multiple layers and backend support.
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 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.
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.
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