SuperSpec: Context Engineering and BDD for Agentic AI

Shashikant Jagtap

The context engineering practices are getting very popular these days over the prompt engineering. Recent paper published on the survey of the Context engineering for LLMs shown the various techniques, how context can be provided to LLMs. The AI development landscape is undergoing a fundamental transformation. We have covered context engineering in details in our previous blogs and how it can be foundation for the Agent Engineering. We have already the SWE practices like TDD/BDD promoted better code quality applications using iterative outside-i development. As we move from simple prompt engineering to complex multi-agent systems, we need new paradigms for specifying, testing, and deploying intelligent agents. On other hand, the framework like DSPy can play significant role to promote both Context Engineering and BDD practices. SuperSpec (pronounced as /suː.pər spɛk/ ) emerges as the first comprehensive solution that unites Behaviour-Driven Development (BDD) with Context Engineering for the age of autonomous AI.

SuperSpec is a declarative language that lets teams design, test, and iterate on AI agents the same way modern developers use Behaviour-Driven Development (BDD). Instead of scattering prompts, retrieval settings, tool calls, and memory tricks across source code, everything lives in a single, version-controlled YAML playbook. This article walks through the ideas behind SuperSpec, shows how it differs from classic BDD tools like RSpec, and demonstrates why it is a foundational layer for context-first Agent Engineering.

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The Context Engineering Revolution

Context Engineering represents the systematic approach to curating the optimal information environment for Large Language Models. As detailed in the Superagentic AI article, this discipline addresses the critical challenge of providing “just-right” context to AI agents. Traditional prompt engineering focuses on crafting individual prompts. Context Engineering expands this to encompass:
  • Dynamic knowledge retrieval through RAG systems
  • Persistent memory management across conversations
  • Tool integration and orchestration
  • Short-term / episodic memory
  • Multi-modal context assembly
  • Context compression and optimization
The goal is delivering precisely calibrated information—enough to enable high-quality responses, but not so much that costs spiral or focus is lost. A detailed discussion can be found in the Superagentic AI post Context Engineering: Path towards better Agent Engineering. You should always checkout the survey paper that published recently her. 

Agent Engineering: The New Discipline

Agent Engineering is the discipline of turning an LLM into an autonomous, goal-driven entity that plans, reasons, calls tools, stores memories, and remains observable and safe. Agent Engineering represents the evolution of software engineering for autonomous systems. It’s built around the IMPACT framework:
  1. Integrated LLMs — Central language models with optimized configurations
  1. Meaningful intent & goals — Clear, measurable objectives
  1. Plan-driven control flows — Structured reasoning pipelines
  1. Adaptive planning loops — Dynamic course correction mechanisms
  1. Centralized persistent memory — Long-term context storage systems
  1. Trust & observability — Safety and transparency mechanisms

Agent Engineering marks a seismic shift in how AI systems are built, deployed, and maintained. It redefines roles, introduces new skill sets, and enables a world where intelligent systems can reason, adapt, and grow. Whether you’re building agents, supervising them, or collaborating with them—the future of AI is Agentic.

SuperSpec: The Declarative Bridge

SuperSpec serves as the declarative interface between Context Engineering (what data enters the model) and Agent Engineering (how the model is orchestrated). It’s a Kubernetes-style specification language that makes agent building as simple as writing a YAML file that transforms agent development from imperative coding to declarative configuration. SuperSpec is our declarative DSL that makes agent building as simple as writing a specification. Think of it as “Kubernetes for AI agents” – you describe what you want, and SuperOptiX builds the entire pipeline. SuperSpec is currently used along with the SuperOptiX framework but it can be used independently. 

It is:
  • Declarative & strongly typed (schema-validated)
  • Test-first (feature specifications run as executable scenarios)
  • Runtime-agnostic (DSPy today; any optimiser tomorrow)

The BDD Connection: From RSpec to SuperSpec

Behaviour-Driven Development(BDD) revolutionised software development by making specifications executable. Tools like RSpec and PHPSpec introduced the Given/When/Then pattern that bridged technical and non-technical stakeholders.
SuperSpec applies this same philosophy to AI agents, but with a crucial difference: the “unit under test” is an entire agent pipeline, not a single function. This requires a new approach to BDD that accounts for the probabilistic nature of LLM outputs.
Aspect RSpec/PHPSpec SuperSpec
Subject Code methods Agent behaviors
Language Ruby/PHP DSL YAML DSL
Scenarios GWT or custom DSL feature_specifications
Assertions Boolean tests Semantic metrics
Feedback Test failures Optimization loops
You can read more about the SuperSpec on Documentation and DSL reference

SuperSpec DSL: Complete Agent Specifications

SuperSpec uses a comprehensive YAML-based DSL that captures every aspect of an agent’s behavior.

apiVersion: agent/v1
kind: AgentSpec
metadata:
  name: "Developer Assistant"
  id: "developer"
  namespace: "software"
  version: "1.0.0"
  agent_type: "Supervised"
  level: "oracles"
  description: "An agent that helps write clean, efficient, and maintainable code"

spec:
  language_model:
    location: "local"
    provider: "ollama"
    model: "llama3.2:1b"
    api_base: "http://localhost:11434"

  persona:
    name: "DevBot"
    role: "Software Developer"
    goal: "Write clean, efficient, and maintainable code"
    traits: ["analytical", "detail-oriented", "problem-solver"]

  tasks:
  - name: "implement_feature"
    instruction: "Implement the feature based on the provided requirement"
    inputs:
    - name: "feature_requirement"
      type: "str"
      description: "A detailed description of the feature to implement"
      required: true
    outputs:
    - name: "implementation"
      type: "str"
      description: "The code implementation of the feature"

  agentflow:
  - name: "generate_code"
    type: "Generate"
    task: "implement_feature"

  evaluation:
    builtin_metrics:
    - name: "answer_exact_match"
      threshold: 1.0

  feature_specifications:
    scenarios:
    - name: "developer_comprehensive_task"
      description: "Given a complex software requirement, the agent should provide detailed analysis"
      input:
        feature_requirement: "Complex software scenario requiring comprehensive analysis"
      expected_output:
        implementation: "Detailed step-by-step analysis with software-specific recommendations"

Key sections in the SuperSpec are

  • metadata – ID, tier, versioning
  • spec.language_model – provider, model size, temperature
  • spec.persona – role, goal, traits
  • tasks – declarative inputs/outputs with instructions
  • agentflow – ordered reasoning / tool-calling steps
  • context – retrieval and memory blocks (Genie only)
  • evaluation – builtin or custom metrics
  • feature_specifications – Inputs and outputs of the system

BDD Feature Specifications: AI-Optimized Testing

The feature_specifications section implements BDD scenarios specifically designed for AI evaluation. Unlike traditional Given/When/Then syntax, SuperSpec uses a structured approach that enables both human readability and machine evaluation:
feature_specifications:
  scenarios:
  - name: "developer_problem_solving"
    description: "When facing software challenges, the agent should demonstrate systematic problem-solving approach"
    input:
      feature_requirement: "Challenging software problem requiring creative solutions"
    expected_output:
      implementation: "Structured problem-solving approach with multiple solution options"
Each scenario serves multiple purposes:

  1. Human-readable documentation of expected behaviors
  1. Training data for DSPy optimization loops
  1. Test cases for automated evaluation
  2. Quality gates for deployment decisions

Professional BDD Runner: Production-Grade Testing

SuperOptiX includes a sophisticated BDD specification runner that provides enterprise-level testing capabilities:
# Standard specification execution
super agent evaluate developer

# Detailed analysis with verbose output
super agent evaluate developer --verbose

# Auto-tuning for improved results
super agent evaluate developer --auto-tune

# JSON output for CI/CD integration
super agent evaluate developer --format json
The runner provides comprehensive evaluation using multiple criteria:

  • Semantic Similarity (50% weight) – How closely output matches expected meaning
  • Keyword Presence (20% weight) – Important terms and concepts inclusion
  • Structure Match (20% weight) – Format, length, and organization similarity
  • Output Length (10% weight) – Basic sanity check for response completeness
Quality gates ensure reliable deployment:

  • ≥ 80%: EXCELLENT – Production ready
  • 60-79%: GOOD – Minor improvements needed
  • < 60%: NEEDS WORK – Significant improvements required

Context Engineering at Scale

SuperSpec can extend the context engineering practices that can be integrated with the memory and vector databases. SuperSpec elevates context engineering from ad-hoc practices to systematic specification:
spec:
  memory:
    enabled: true
    short_term:
      enabled: true
      max_tokens: 2000
      window_size: 10
    long_term:
      enabled: true
      storage_type: "local"
      max_entries: 500
      persistence: true
    episodic:
      enabled: true
      max_episodes: 100
      episode_retention: 30
    context_manager:
      enabled: true
      max_context_length: 4000
      context_strategy: "sliding_window"

  retrieval:
    enabled: true
    retriever_type: "chroma"
    config:
      top_k: 5
      chunk_size: 512
      chunk_overlap: 50
    vector_store:
      embedding_model: "sentence-transformers/all-MiniLM-L6-v2"
      collection_name: "agent_knowledge"
You can configure SuperSpec for the advanced configurations, refer the DSL reference here

DSPy Integration: Optimization-First Development

SuperSpec integrates seamlessly with DSPy’s evaluation-first methodology:
spec:
  optimization:
    strategy: "few_shot_bootstrapping"
    metric: "answer_correctness"
    metric_threshold: 0.8
    few_shot_bootstrapping_config:
      max_bootstrapped_demos: 4
      max_rounds: 1

  evaluation:
    builtin_metrics:
    - name: "answer_correctness"
      threshold: 0.8
      weight: 2.0
    - name: "response_quality"
      threshold: 0.7
    - name: "safety_compliance"
      threshold: 1.0
      weight: 3.0
The workflow becomes:

  1. Write SuperSpec with BDD scenarios
  1. Compile to DSPy pipeline
  1. Evaluate baseline performance
  1. Optimize automatically using scenarios as training data
  1. Re-evaluate to measure improvement
  1. Deploy when quality gates pass

How SuperSpec Context become Powerful DSPy Signature

SuperSpec fields like persona, Task input and output become DSPy signature that can be further customised by the DSPy experts if needed for further prompt a context optimization. Let’s take a simple example example for software developer agent with provided context in the SuperSpec YAML

persona:
    name: DevBot
    role: Software Developer
    goal: Write clean, efficient, and maintainable code
    traits:
    - analytical
    - detail-oriented
    - problem-solver
  tasks:
  - name: implement_feature
    instruction: You are a Software Developer. Your goal is to write clean, efficient,
      and maintainable code. Implement the feature based on the provided requirement.
    inputs:
    - name: feature_requirement
      type: str
      description: A detailed description of the feature to implement.
      required: true
    outputs:
    - name: implementation
      type: str
      description: The code implementation of the feature.

With given Spec, when the SuperOptix can compile this into powerful DSPy signature using command, super agent compile <agent_name>  and it produces the Signature code by default. The example of the spec that used above might produce the DSPy Signature like this:

# ==============================================================================
# 1. DSPy Signature (Input / Output Schema) – CUSTOM LOGIC
# ==============================================================================

class DeveloperSignature(dspy.Signature):
    """
    Software Developer: Write clean, efficient, and maintainable code
    
    Role: Software Developer    Traits: analytical, detail-oriented, problem-solver    
    Instruction: You are a Software Developer. Your goal is to write clean, efficient, and maintainable code. Implement the feature based on the provided requirement.    """
    # Input Fields
    feature_requirement: str = dspy.InputField(desc="A detailed description of the feature to implement.")

    # Output Fields
    reasoning: str = dspy.OutputField(desc="The step-by-step reasoning process to arrive at the answer.")
    implementation: str = dspy.OutputField(desc="The code implementation of the feature.")

This auto-generated DSPy signature can be further tuned if you DSPy expert to make it more powerful.

Beyond DSPy: Framework-Agnostic Future

While SuperSpec currently targets DSPy, its declarative nature enables expansion to other frameworks. The possibilities are endless for SuperSpec. Here are some possible integration with other frameworks. 
  • LangChain adaptation: Map agentflow to chain components
  • Custom optimizers: Plug in RLHF, PEFT, or proprietary techniques
  • Cloud deployment: Generate serverless function configurations
  • Kubernetes orchestration: Transform specs into CRDs for large-scale deployment
Because SuperSpec is purely declarative:
  • A LangChain/Graph compiler could map agentflow steps to SequentialChain nodes.
  • A TGI or vLLM backend can be swapped by editing language_model only.
  • Custom optimisation strategies (RLHF, PEFT) plug in via the optimization section.

The SuperSpec Advantage

SuperSpec delivers transformative benefits for AI development teams:
  • Single source of truth: Persona, context, flow, testing, and optimization in one versioned file
  • Shift-left reliability: BDD scenarios catch hallucinations before deployment
  • Runtime agnosticism: Swap backends without changing specifications
  • Team communication: Product managers and engineers work from the same specifications
  • Version control: Track changes to agent behavior over time

The Future of Agent Development

SuperSpec represents a paradigm shift toward specification-first agent development. By combining the rigor of BDD with the sophistication of modern context engineering, it transforms AI development from art to engineering discipline.
Teams can now:

  • Design agents declaratively using industry-standard YAML
  • Test behavior systematically with executable specifications
  • Optimize automatically using proven ML techniques
  • Deploy confidently with comprehensive quality gates
As AI systems become increasingly complex, SuperSpec provides the foundation for maintainable, reliable, and auditable intelligent systems. It’s not just a specification language—it’s the future of how we build AI that works. 

Final Thought

SuperSpec fuses context engineering and BDD into a coherent workflow: write YAML, validate, run scenarios, optimise, and deploy.
By elevating context to a first-class, testable artefact, it turns agent engineering into an engineering discipline with the same rigour developers expect from software pipelines. Start with a simple Oracles playbook, evolve into a Genies with tools, RAG, and memory, and let SuperSpec guide the journey—all without touching Python.
SuperSpec is available as part of the SuperOptiX framework. Learn more at the official documentation or DSL reference and start building your production-worthy AI agents.