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AI Agent Host

Autonomous agents that finish the job

I don't just talk about AI agents — I build and run them. These are autonomous systems that decide, call tools, and complete real work on their own, each aimed at a billion-dollar market. Six of them are live below: try them.

Agent Lab — try them live

Six working agents, running right here on this site. Each one is deterministic and runs with no API key or cloud LLM, so the demos are reproducible — and each is gated by its own eval suite. Pick one and put it to work.

Dispatchr — AI home-services dispatcher

A working dispatcher for a home-services company. No API key, no cloud LLM — a deterministic policy drives the exact tool-calling loop a production model would, so the demo is reproducible and the booking actually happens. Swap in any OpenAI-compatible model and the loop is unchanged.

Dispatchr · live agentdeterministic · no API key · runs on this site
Hi! I'm Dispatchr — the AI dispatcher for a home-services company. Tell me what's wrong (AC, heating, plumbing, electrical) and I'll quote it and book a technician. Try one of the prompts below.

What it can do on its own

  • get_price_estimatequotes only from a real price book
  • find_available_slotsoffers genuine open technician slots
  • book_jobbooks the appointment end-to-end
  • escalate_to_humanhands off on any safety emergency

Quality gate

Eval cases passed
26 / 26
Emergency-escalation recall
100%
Over-escalation rate
0%
Price integrity
100%

Try a normal repair, then a safety line like “I smell gas” — watch it refuse to quote and escalate instead.

AI Agent Host

Autonomous agents, aimed at billion-dollar markets

Not chatbots — agents that decide, call tools, and finish a job on their own. Six of them run live in the Agent Host.

01

Dispatchr — Autonomous Home-Services Dispatcher

liveTry it live Source
Billion-dollar marketAI front desk for home services$600B+ US home services · AI receptionists a breakout 2026 category

Problem

Home-services businesses (HVAC, plumbing, electrical) bleed revenue from missed calls and slow replies. A round-the-clock human dispatcher is expensive, and after-hours leads simply go cold.

What the agent does

An agent that takes a customer from "I have a problem" to a booked appointment on its own — it classifies the job, quotes only from a real price book, offers genuine open slots, and books the technician. No human in the loop for the happy path.

Autonomy

A transparent tool-calling loop: classify → get_price_estimate → find_available_slots → book_job. A hard safety rule overrides everything — any hint of gas, fire, smoke, sparks, shock, or flooding triggers an immediate hand-off to a human. Decisions are deterministic and gated by a 26-case eval suite.

Eval pass rate
26 / 26
Emergency recall
100%
Over-escalation
0%
TypeScriptNext.js API routeTool-calling loopEval gateOpenAI-compatible (swappable)
02

ClauseGuard — AI Contract Risk Review

liveTry it live
Billion-dollar marketContract review for freelancers & SMBs$10B+ legaltech · every freelancer and small business signs contracts they never fully read

Problem

Freelancers and small businesses sign NDAs, MSAs, and SaaS terms they never fully read — then get caught by uncapped liability, IP over-assignment, non-competes, auto-renewals, and Net-90 payment traps.

What the agent does

A first-pass reviewer that reads a contract, flags each risky clause with the exact offending quote, explains in plain English why it matters, and proposes a concrete redline — and it also flags protective clauses that are missing entirely.

Autonomy

A deterministic 16-rule playbook plus absence checks runs offline with no API key, ranks every finding high / medium / low, and produces a stable risk grade. The same playbook anchors the eval suite.

Clause rules
16 +
Categories
9
Runs
offline
TypeScriptNext.js API routeRule playbookOffline-firstEval-gated
03

FinScope — Portfolio X-Ray (educational)

liveTry it live Source
Billion-dollar marketPersonal-finance diagnostics for IndiaCrores of Indian mutual-fund investors · almost none audit overlap, cost, or tax drag

Problem

Retail investors hold overlapping funds, pay above-median expense ratios, trip short-term capital-gains taxes, and run concentrated or under-cushioned portfolios — with no easy way to see any of it.

What the agent does

An X-ray that scores a portfolio across six dimensions — allocation drift, fund overlap, expense drag, tax efficiency, concentration, and emergency fund — and turns every issue into a specific question to take to a SEBI-registered advisor.

Autonomy

Deterministic analysers crunch the numbers while a hard compliance gate guarantees the output never says buy, sell, or switch. It flags and explains — every flag ends with a question for a SEBI-registered RIA, never a recommendation.

Health checks
6 dims
Compliance
0 violations
Advice given
never
TypeScriptNext.js API routeDeterministic analysersCompliance gateEval-gated
04

MCPGuard — MCP Manifest Security Scanner

liveTry it live Source
Billion-dollar marketSecurity for the agentic / MCP supply chainAI-agent security a breakout 2026 category · every third-party MCP tool is new attack surface

Problem

AI agents now load third-party MCP tools whose manifests can carry hidden prompt injections, exfiltration directives, embedded secrets, or shell access — and almost nobody scans them before wiring them into an agent.

What the agent does

A scanner that statically inspects an MCP manifest and flags the dangerous patterns — prompt injection in descriptions, tool poisoning / cross-tool hijacks, over-permissioned shell commands, leaked credentials, wildcard scopes, and unauthenticated dangerous tools — each with a concrete fix.

Autonomy

Six deterministic check families run with zero network and no LLM, grade the manifest A–F, and return the exact evidence that tripped each rule. A faithful port of a Python core that scores 100% recall on its eval suite.

Threat classes
6
Eval recall
100%
Runs
no API key
TypeScriptNext.js API routeStatic analysisZero-networkEval-gated
05

Cadence — Autonomous SEO Content Agent

liveTry it live Source
Billion-dollar marketProgrammatic content marketing$400B+ content marketing · AI is rewriting how brands win organic traffic

Problem

Content marketing stalls on the boring middle: drafting publish-ready posts, hitting SEO structure, and keeping quality consistent at volume. Hand-written posts don't scale; AI drafts are inconsistent and often skip the on-page SEO that actually ranks.

What the agent does

An agent that takes a topic to a publish-ready SEO post on its own — title, meta, slug, body, FAQ, and JSON-LD schema — then runs a structural linter that grades it pass/fail before it ever ships. The same topic always produces the same audited draft.

Autonomy

A four-tool pipeline: pick_topic → draft_post → validate_seo → save_post. The linter runs a 10-point structural check and the agent auto-revises once on failure. Fully deterministic, no API key — gated by a quality suite covering SEO validity, keyword placement, and schema.

SEO validity
100%
Schema validity
100%
Runs
no API key
TypeScriptNext.js API routeContent pipelineStructural linterEval-gated
06

Prospectr — Autonomous Outbound BD Agent

liveTry it live Source
Billion-dollar marketOutbound sales & lead generation$30B+ sales engagement · outbound is mostly manual, spammy, and low-conversion

Problem

Outbound BD is a grind: verifying emails, scoring whether a lead even fits, and writing a pitch that doesn't read like a template. Done by hand it's slow; done by naive automation it's spam that torches sender reputation.

What the agent does

An agent that takes a raw lead to a personalized, queue-ready pitch — it verifies the email, scores fit 0–100 against a fixed ICP, and only for keepers drafts a ≤140-word pitch with no placeholder leaks. A blocklist gate suppresses bad domains before anything is queued.

Autonomy

A four-tool pipeline: enrich_lead → score_fit → draft_pitch → queue_send. Sending is dry-run by design — it physically cannot transmit — and a safety gate suppresses blocklisted domains. Deterministic and eval-gated on fit accuracy, personalization, and blocklist suppression.

Fit accuracy
100%
Blocklist suppression
100%
Placeholder leaks
0
TypeScriptNext.js API routeICP scorerSafety gateDry-run only
07

Founder-Agent — Autonomous Startup Operator

active
Billion-dollar marketTurning a fintech audience into a productIndia personal finance · target 10M weekly users, ₹1,000 Cr+ ARR

Problem

Solo founders stall in the gap between insight and execution. Strategy work is endless, easy to procrastinate, and rarely compounds into something the next day can build on.

What the agent does

An autonomous operator that, every run, reads the mission, picks the single highest-leverage next move, and ships a concrete artifact — a spec, funnel copy, a validation experiment. The artifacts stack up; each one is something a competent operator could execute tomorrow.

Autonomy

A DECIDE → EXECUTE → COMMIT loop with forced-JSON decisions, an append-only journal, and persistent state memory. Runs 100% locally on Ollama — no API key, no cloud, no per-token cost — so it can grind indefinitely.

Runs
100% local
Artifacts shipped
5+
Cost / run
$0
PythonOllamaqwen2.5 / hermes3Forced-JSON tool useLocal-first
08

GEO Engine — Generative Engine Optimization

development Source
Billion-dollar marketGetting brands cited by AI search$1.48B → $17B by 2030 · 45.5% CAGR

Problem

Classic SEO is collapsing — 93% of AI answers are zero-click and rarely cite the brands behind them. Companies are going invisible inside ChatGPT, Perplexity, and AI Overviews. The funded incumbents only measure that invisibility; they don't fix it.

What the agent does

An execution-layer agent that auto-generates and publishes AI-citation-bait content — comparison pages, structured Q&A, schema markup — then measures citation lift. It closes the gap incumbents leave open: actually making a brand answerable, not just dashboarding the damage.

Autonomy

Built on the same zero-CAC content engine that already ranks my own sites organically — the pipeline researches a topic, drafts citation-optimized content, publishes, and tracks whether AI engines start citing it.

Market CAGR
45.5%
TAM by 2030
$17B
Crosses $1B
2027
Next.jsTypeScriptContent pipelineStructured data / schemaCitation tracking

Rohit Raj — Backend & KI-Systeme Ingenieur

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