Agents that actually finish the job.
Not chatbots that wait for the next message. AI agents that plan, call tools, retry, and hand off to humans only when they should — running your ops loop autonomously.
- 🧠 Plan · resolve email → company → tech stack
- 🔧 Tool · clearbit.company_lookup(...)
- 🔧 Tool · builtwith.stack(domain=acme.com)
- 🤔 Reflect · missing headcount → try apollo
- 🔧 Tool · apollo.enrich(...)
- ✍️ Compose · CRM note with 5 datapoints
- 🔒 Approval · post to HubSpot? (auto-approved · policy: enrich)
- ✅ Done · 42s · ₹4.10 · tokens 3,214
Kinds of agents we build.
Ops agents
Ticket triage, incident response, on-call summaries.
Research agents
Company deep-dives, competitor scans, market maps.
Outreach agents
Personalised sequences at scale with quality gates.
Compliance agents
Doc review, policy checks, red-flag surfacing.
Rules we ship with.
The gap between a demo and a production agent is the boring stuff. We do the boring stuff.
- Narrow scope per agent
- Tool allow-list only
- Budget + step limits
- Approval gates for irreversible actions
- Full trace + replay logs
- Fallback to human on low confidence
- Test suite of scenarios
- Weekly performance review
Agent FAQs.
Unstructured ones are. We build agents with narrow scopes, tool allow-lists, retry logic, and approval gates. Reliability comes from architecture, not the model.
Agent architecture patterns
Pick the topology by task shape — most "unreliable agent" stories are the wrong pattern applied to the wrong job.
| Pattern | When it fits | Failure mode | Reliability lever |
|---|---|---|---|
| Single-step tool use | One decision, few tools | LLM picks wrong tool | Constrained function-calling schemas |
| ReAct loop | Research, enrichment | Loops until budget dies | Hard step + token caps |
| Plan-and-execute | Multi-hour workflows | Stale plan mid-run | Re-plan checkpoint every N steps |
| Router + specialists | Mixed intents | Router misroutes edge cases | Fallback specialist + confidence gate |
| Hierarchical (supervisor) | Cross-team ops | Supervisor becomes bottleneck | Parallel workers + async join |
| Human-in-the-loop | Irreversible or costly acts | Approval fatigue | Auto-approve within policy envelope |
Failure modes and the guardrails that catch them
A production agent is 20% prompt, 80% guardrails. These are the ones we ship with by default.
- Runaway tool calls
Per-run token + step + wall-clock budget; agent hard-stops and escalates to human with the partial trace.
- Hallucinated tool arguments
Zod/JSON-schema validation on every tool call; failed validation counts as a step and forces re-plan.
- Silent data corruption
All writes go through an idempotency key; retries never double-post; every mutation logged with before/after.
- Prompt injection via retrieved docs
Untrusted content wrapped in delimiters, system prompt reasserted, tools blocked from reading user-controlled URLs.
- Cost drift
Per-workflow cost budget with weekly alerting; cache identical sub-queries; downgrade model on non-reasoning steps.
- Silent regressions on model updates
Golden test set of 30-100 scenarios run on every model or prompt change; block deploy on regression.
Put an agent on it.
Design a pilotServing businesses across Rayalaseema
Founder-led website design, development and automation — for businesses in Andhra Pradesh and beyond.
Automate what's costing your team hours
Describe the workflow — we'll reply with what's possible, what it costs, and where to start.
- Personal reply from Siddiq within 24 hours
- Honest advice — no pushy sales, no fake urgency
- Clear next steps, timeline, and estimate