We often equate the arrival of powerful LLMs with an automatic leap in capability. The truth is subtler: raw generative power is only half the equation. The other half is reliable orchestration – the ability to ground model output in real-time signals, enforced contracts, and auditable tooling. A recent demo that combines a search tool and a custom function in a single model call nicely exposes why that second half matters for production systems.
Context – the signal, not the noise
I recently reviewed a demonstration that sent a single request to a generative model configured with two “tools”: a built‑in Google search and a developer-declared function (getWeather). The model executed a two-turn flow – first invoking search and requesting the function, then the developer returning the function result and asking the model for a final synthesis. The demo surfaced execution metadata (function_call ids, parts, thought signatures) and highlighted server-side tool invocations.
Why this pattern matters for architects
1. Grounding reduces hallucination – but only when the pipeline is designed end-to-end.
Allowing the model to call an external search tool and then a deterministic function changes the failure model. Instead of trusting a single free‑text response, we now have a chain: external data → function contract → model synthesis. That reduces hallucination risk, provided the tool outputs are validated and versioned. In enterprise terms, it’s the difference between relying on an “opinion” and consuming an auditable data stream.
2. Treat function declarations as API contracts.
The demo uses explicit schema declarations for the function (types.Schema). That’s essential: when you move models into workflows, function signatures become your contract with the model. Architecturally, these should be first-class artifacts in your CI/CD – versioned, tested, and backward compatible.
3. Observability and reproducibility are non-negotiable.
The demo captures function_call IDs and returns model “parts” and thought signatures. For regulated, mission‑critical, or public-sector systems, that telemetry is gold: it enables debugging, incident forensics, and compliance audits. Don’t deploy model-driven pipelines without logging tool invocations, timestamps, and the exact inputs supplied to the model for each turn.
4. Security & data governance must be designed into tool usage.
Server-side tool invocations and external searches can leak sensitive context if not controlled. Enterprises – and especially public digital infrastructures – must enforce data residency and field-level redaction, encrypt tool payloads in transit and at rest, and ensure that function calls execute in trusted environments. Compliance cannot be an afterthought.
5. Build vs. buy: choose the right glue.
This pattern highlights a pragmatic “build small, orchestrate big” approach. Use vendor models for language synthesis and retrieval, but write small, testable functions for authoritative data (weather, financial data, identity lookup). That minimizes vendor lock-in while keeping the model as the orchestration and synthesis layer.
Concrete guidance for CTOs and founders
– Define function schemas early and keep them in Git alongside your API specifications. Treat them as contracts.
– Make functions idempotent and safe to replay. Include unique request IDs in every invocation.
– Validate all tool responses with structural checks and business rules before feeding them back to the model.
– Implement rich observability: record full tool inputs/outputs, function_call ids, and model part metadata for each session.
– Design privacy controls per data class: what can be searched externally, what must stay on-prem, and what needs ephemeral memory.
– Stage rollouts: simulate tool failures and stale data scenarios – the model should fail gracefully or revert to human-in-the-loop.
A note for Digital Public Infrastructure in India
This approach has direct relevance for e‑Governance and DPI. When models are asked to synthesize citizen-facing answers, grounding them in trusted government APIs (registered, auditable functions) can preserve accuracy and legal compliance. In regions like Northeast India, where trust and data sensitivity are paramount, explicitly enforcing server-side tool policies and local data handling is not merely technical prudence – it’s civic responsibility.
Takeaways
– Models are powerful synthesizers; they aren’t authoritative systems of record.
– Ground LLM outputs with deterministic, auditable functions and trusted data sources.
– Treat function declarations and tool invocations as first-class engineering artifacts.
– Build observability, governance, and staged deployment into your AI architecture from day one.
Closing thought
The most transformative part of generative AI won’t be single-turn fluency; it will be systems that reliably marry that fluency with trustworthy tooling and governance. Design for that marriage first – the rest becomes an implementation detail.
About the Author Sanjeev Sarma is the Founder Director of Webx Technologies Private Limited, a leading Technology Consulting firm with over two decades of experience. A seasoned technology strategist and Chief Software Architect, he specializes in Enterprise Software Architecture, Cloud-Native Applications, AI-Driven Platforms, and Mobile-First Solutions. Recognized as a “Technology Hero” by Microsoft for his pioneering work in e-Governance, Sanjeev actively advises state and central technology committees, including the Advisory Board for Software Technology Parks of India (STPI) across multiple Northeast Indian states. He is also the Managing Editor for Mahabahu.com, an international journal. Passionate about fostering innovation, he actively mentors aspiring entrepreneurs and leads transformative digital solutions for enterprises and government sectors from his base in Northeast India.
