Agent-as-a-Service
Agent-as-a-Service finish the job.
Most demos of AI agents impress for ninety seconds and break on the first edge case. Production agents need harnesses, retries, evals, observability, and a kill switch. We build the boring parts.
- Single-shot agents · multi-step planners · multi-agent orchestrations.
- MCP, OpenAI Agents SDK, LangGraph, AutoGen, custom — picked by fit.
- Eval harness with deterministic golden tasks and probabilistic checks.
- Cost ceiling per run + automatic fallback to deterministic when the agent loses the plot.
“An agent without an eval harness is a vibe. An agent with one is a service.”
§ 02 / Authority
From the roomsI've been in — and the feeds that came out of them.
Live shorts. LinkedIn originals. Unedited.
Read all 11 field notes →
PMI Gujarat · Keynote · Dec 2024
I had the incredible opportunity to be part of the PMI Gujarat Chapter — NEXTGEN Project Management, elevating our world through emerging technologies.
Live · this quarter
48,000+
LinkedIn impressions
- Field notes11
- Industries20
- Years shipping15+
Karnavati · AI Conclave 2025
Unlocking the Power of AI: it's all about the right prompt. At Karnavati University, I had the privilege of sharing a key insight…
Rajkot · Keynote · 2025
What happens when AI meets India's social and rural future? You get a room full of students, academics, and leaders in Rajkot asking the questions that actually matter.
PM Forum · Off-script · 2025
I walked into a room of 45+ project managers. No slides. No deck. No script.
- PMI Gujarat
- Karnavati University
- Atmiya AI Summit
- HP WeRise · Ahmedabad
- Project Managers' Forum
- NIFT Gandhinagar
- MeitY · Digital India
- UIT Karnavati
- Ahmedabad founder circle
- PMI Gujarat
- Karnavati University
- Atmiya AI Summit
- HP WeRise · Ahmedabad
- Project Managers' Forum
- NIFT Gandhinagar
- MeitY · Digital India
- UIT Karnavati
- Ahmedabad founder circle
What this means in practice
We design the agent contract first (what it must do, what it must never do, what it costs), then the harness, then the model choice. Models swap; harnesses are forever.
- 01
Agent contract + decision rubric (what counts as success / refusal / escalation).
- 02
Harness with retry, fallback, cost cap, and a kill switch.
- 03
Eval suite — 50–200 golden tasks, regression-tested on every prompt change.
- 04
Observability: per-run trace, token-spend, latency, success rate, drift alerts.
Engagement shapes
3 ways to engage on Agent-as-a-Service. Pick the closest fit; we calibrate scope on the diagnostic call.
01
2–3 wk
Single-shot agent
One narrow job — e.g. invoice triage, lead scoring, support deflection. Tool use + evals. Ships standalone.
02
4–6 wk
Multi-step workflow
An agent that plans, calls tools, and stitches results across 5–15 steps. Used for ops automation, document workflows, deep research.
03
8–12 wk
Multi-agent orchestration
Specialist agents coordinated by a supervisor. Used when no single agent can hold the full context (cross-domain or long-horizon work).
For
Who this fits
- Teams with a real, measurable workflow they want to automate — not a generic chatbot wishlist.
- Engineering orgs already running structured outputs in production who now need stateful agents.
- Operators automating an internal process before scaling it across teams.
Not for
Honest gates
- "AI girlfriend" / vibe-coded character agents.
- Anyone hoping for an autonomous agent that runs forever without an eval harness.
- Pure research benchmarks with no business outcome attached.
FAQ
About this service.
Can you work with our existing agent framework?
Yes. We've built on LangGraph, AutoGen, CrewAI, Pydantic-AI, OpenAI Agents SDK, and several internally-grown harnesses. The framework matters less than the contract; we usually keep what your team is comfortable with and rewrite the harness layer underneath.How do you handle agent failures in production?
Three layers. (1) Cost ceiling per run — if the agent loops, the harness aborts. (2) Deterministic fallback — if the agent returns a low-confidence result, we route to a rules-based or human path. (3) Eval suite catches regressions before deploy; runtime alerting catches drift after.How do you measure agent quality?
Golden tasks (50–200 per agent) covering the success / refusal / escalation rubric. Each task scored against ground truth. Probabilistic checks for the cases where ground truth is fuzzy. CI runs the suite on every prompt change; production runs it on a sampled basis as drift detection.Can the agent be private / on-prem?
Yes — open-weight model + vLLM / Ollama / NIM, plus your private vector DB. Throughput and capability take a step down vs frontier models, but you keep data residency. See the Sovereign AI service for the architecture details.Will this scale to N concurrent agents?
We sizing-math from first principles per project — token budget × concurrency × failover. The harness is async / queue-backed so horizontal scale is mostly a capacity question. The bottleneck is usually the upstream API rate limit, not our code.What about prompt injection?
Treated as a first-class threat from the architecture phase. Tool-call allow-lists, user-input sanitisation at the boundary, separate tenancy for memory, and red-team eval cases. Real-world coverage isn't 100% — anyone claiming it is, isn't being honest.
Related services
You may also need these.
01
Multi-agent Systems
Specialist agents, supervised. Coordination is the product.
Read the page →02
LLMOps
Evals, observability, and deploys that survive Monday morning.
Read the page →03
AI-as-a-Service
Strategy → architecture → ship — one accountable engineer.
Read the page →
Voices from the rooms
48,000+ impressions · 11 notes · 9 venues
PMI Gujarat · Keynote · Dec 2024 Karnavati · AI Conclave 2025 Rajkot · Keynote · 2025 PM Forum · Off-script · 2025
Next step
Tell me what you’re trying to ship.
One brief, one inbox, one reply within a business day. Either a calendar slot, a referral, or a straight no — we don’t bench-fit work that isn’t a fit.