Agentic AI applications built with LangChain & Haystack — grounded, explainable, and production-ready.

Haystacked exists to build real AI agents — not demos.

We design agentic AI applications that connect LLMs to real systems: databases, vector stores, and external tools — with correctness, transparency, and production constraints as first-class concerns.

LLMs are powerful.
Agents are responsible.

• 🤖 Agentic AI systems using LangChain and Haystack
• 🧰 Tool-orchestrated workflows (SQL, retrieval, web search, APIs)
• 📊 Data-grounded reasoning over structured and unstructured sources
• 🔐 Production-safe execution with guardrails and observability
• ✅ Explainable answers backed by traceable evidence

This is not prompt engineering.
This is systems engineering with LLMs.

flowchart LR
    Q[User Query] --> A[Agent]
    A -->|Plan| R[Tool Router]

    R -->|SQL| DB1[Deal Table]
    R -->|SQL| DB2[Canada Wages DB]
    R -->|Vector| VS[Vector Store]
    R -->|Web| WEB[External Search]

    DB1 --> A
    DB2 --> A
    VS --> A
    WEB --> A

    A --> L[LLM Reasoning]
    L --> O[Grounded & Explainable Answer]

• Schema-aware query generation
• Read-only execution
• Parameterized queries
• Row limits and timeouts

• Semantic similarity search
• Context injection for reasoning
• Historical and domain grounding

• Real-time data enrichment
• Clearly labeled non-authoritative sources
• Never overrides internal data

Every agent execution is constrained by design:

• ❌ No data mutation (INSERT, UPDATE, DELETE, DROP)
• ✅ SELECT-only SQL queries
• ⏱ Execution time limits
• 📉 Result size caps
• 🔍 Pre-execution validation

def validate_sql(query: str) -> None:
    assert query.strip().lower().startswith("select")
    forbidden = ["insert", "update", "delete", "drop", "alter"]
    for keyword in forbidden:
        if keyword in query.lower():
            raise ValueError("Forbidden SQL operation")

Every response produced by Haystacked includes:

• Answer — the conclusion
• Sources — where the data came from
• Reasoning — how the conclusion was derived

Example:

Answer:
Average wage growth for Ontario tech roles is 4.2%.

Sources:
- Canada Wages Database (2023–2024)
- Vector context: regional labor trends

Reasoning:
Filtered by province and industry, then compared year-over-year averages.

• “What’s the average wage growth for tech roles in Ontario?”
• “Compare fintech deal sizes between Q2 and Q3”
• “Are deal trends aligned with labor market signals?”
• “Summarize financial risks using current wage data”

• Python 3.13
• LangChain — agent orchestration
• Haystack — retrieval & document pipelines
• SQL (Postgres / MySQL) — authoritative data access
• Vector Stores (pgvector / Chroma) — semantic retrieval
• LLMs (GPT-5 class) — reasoning & synthesis

• Deterministic tool execution
• Modular, extensible agent design
• Framework-agnostic architecture
• Observability-ready reasoning traces

• Multi-agent collaboration
• Cost-aware routing
• Caching & replay
• Human-in-the-loop review
• Visualization of reasoning traces

Haystacked — AI agents that reason over reality.