AI AGENT DEVELOPMENT
AI agents that ship — and stay shipped.
Agent demos are easy. Agent reliability is hard. We build evaluated, observable agents that complete real tasks in production — with the guardrails, evals, and human-in-the-loop fallbacks that demos skip.
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Built for teams that can't afford to guess.
Agent demos are easy. Agent reliability is hard. We build evaluated, observable agents that complete real tasks in production — with the guardrails, evals, and human-in-the-loop fallbacks that demos skip.
Most agents work in a demo. Few work in production.
Someone builds an agent that handles a happy-path task in a notebook, the team gets excited, and six months later it's still in pilot — because as soon as it meets real data, the success rate craters from 95% to 40%.
The reasons are predictable, and they're not about the model. They're about everything around the model: tool reliability, memory boundaries, error handling, eval coverage, observability, and designing for failure modes instead of demos.
Six classes of agents we ship into production.
Single-purpose task agents
One agent, one tool surface, one job — research, summarization, classification, extraction.
Multi-agent systems
Coordinator + specialists. An orchestrator delegates to focused worker agents.
Browser-using agents
Navigate, click, fill forms, extract from web UIs. Playwright, Browserbase, computer-use.
Voice agents
Inbound/outbound phone agents on Live Kit, Vapi, Retell. Support, qualification, scheduling.
Customer-support copilots
Tier-1 agents with KB retrieval, ticket triage, escalation, tone control.
Internal ops agents
Database queries, ETL, finance reconciliation, Rev Ops. Boring, valuable, high-ROI.
Evals before agents.
Most teams build the agent first and figure out evals later. We do it backwards.
Define success, in writing
Concrete, measurable rubrics for what 'the agent did the task correctly' means.
Build the golden dataset
50–500 real examples with correct answers. The harness everything runs against.
Build against the harness
Every prompt iteration, model swap, and tool change runs the full eval. Regressions block merge.
Shadow production traffic
Run in shadow mode against real traffic before serving users. Diff against humans, then promote.
What “production-ready” actually requires.
Pydantic / Zod on every tool boundary, with structured errors the agent can recover from.
Every external call has a retry policy and a fallback. Repeated failures circuit-break and escalate.
High-stakes actions pause and ask a human. Configurable per workflow.
Hard cost ceilings and latency targets. Runaway agents are killed and logged.
Every LLM call, tool call, and decision tagged. Production failures are replayable.
CI runs the golden-dataset eval on every commit. Regressions block deploy.
How agent projects engage with us.
Agents are a build + tune + observe loop. Our engagements are sized accordingly.
Related reading
Give your agents a private model and the retrieval to back it.
Tell us what you want the agent to do.
Bring us the task. We'll tell you honestly whether it's agent-shaped, whether it's eval-ready, and what the right engagement looks like.
Ready to build?
Let's build your next intelligent platform.
Share your goals — we'll recommend a model, timeline, and team that fits Aanandi Technosoft.
Frequently asked questions
What success rate should I expect?+
Depends on the task. Narrow tasks (classification, extraction, routing): 85–95%. Complex multi-step tasks: 70–85% with human-in-the-loop on the long tail. Anyone promising 99% on a complex task is showing you the demo set.
How do you measure agent quality?+
Measurable rubrics, a golden dataset of 50–500 real examples, scoring via LLM-as-judge plus structured assertions, tracked over time, with regression gates in CI.
Single agent or multi-agent?+
Single-agent first, always. Multi-agent earns its complexity only when role separation genuinely helps quality.
What models do you use?+
Mixed — frontier models for reasoning, smaller open models for classification and routine tool calls. Often multiple models in one agent.
Self-hosted or hosted models?+
Both. Hosted APIs win on capability and speed; self-hosted wins on privacy, cost at scale, and latency. See our Private LLM page.
What happens when a model gets deprecated?+
The retainer covers model migrations. We re-run the eval harness against the candidate, port if it's better, roll back if it isn't.