Weekly Radar sample

A free weekly sample brief for MCP, AI agent, and LLM infrastructure signals

Weekly Radar is a public-source research brief that shows what to review, why it may matter, and which records should stay in a watchlist before outreach. This beta sample shows the format without inflated growth claims.

What Weekly Radar is

A lightweight analyst brief for teams watching AI infrastructure markets.

Built from public-source research and manual verification
Designed for weekly review, category tracking, and lead prioritization
Useful before outreach, partnership mapping, or investor research

This week's free sample structure

This public page is the free beta sample issue format, not a claim of a full live dataset.

5 public-source watchlist signals
3 growing categories to monitor
3 buyer-specific notes for AI security, LLM gateway, and AI observability teams
How to use the brief and what paid Monthly Radar adds

Signal 1: MCP tool access surfaces

Watch public MCP servers, connector catalogs, and tool integrations where new permissions or data paths may appear.

Public sources to review: official docs, GitHub repos, MCP directories, integration pages
Why it matters: tool access can create security, gateway, and monitoring questions
Verification status: keep as watchlist until the project scope and maintainer context are clear

Signal 2: Agent orchestration workflows

Track frameworks and apps that are moving from simple prompts toward multi-step agents, tools, memory, or workflow execution.

Public sources to review: release notes, docs, examples, templates, public roadmaps
Why it matters: orchestration often creates needs around monitoring, policy, evals, and reliability
Manual check: confirm whether the project is an active product, open-source framework, or reference example

Signal 3: LLM gateway control patterns

Look for public projects adding routing, fallback, provider abstraction, rate limits, policy controls, or spend visibility.

Public sources to review: gateway docs, proxy repos, changelogs, integration guides
Why it matters: gateway vendors need to know which teams are standardizing model access
Use as a lead only after checking category fit and whether the team is reachable through public business channels

Signal 4: Observability and eval loops

Watch projects documenting traces, eval datasets, prompt/version tracking, replay, debugging, or regression checks.

Public sources to review: docs, GitHub examples, product pages, integration tutorials
Why it matters: teams rarely say they need observability before workflows become hard to debug
Best status: monitor or manual-review until the signal is tied to an active workflow

Signal 5: AI agent security language

Watch public pages that mention prompt injection, data leakage, tool permissions, MCP risk, agent runtime controls, or policy enforcement.

Public sources to review: vendor blogs, security docs, product pages, repos, conference talks
Why it matters: security language can reveal which categories buyers are educating the market around
Boundary: do not treat security content as a confirmed buying signal

3 growing categories to monitor

The free sample highlights category movement qualitatively, without claiming precise market growth.

MCP security and permissions: tool access, identity, sandboxing, and data exposure review
LLM gateways and control planes: routing, spend controls, fallback, and governance
Agent observability and evals: traces, replay, regression checks, and production debugging

Buyer note: AI security

Use the radar to find projects where agents, MCP, browser tools, or external data access may create review needs.

Prioritize records with clear tool permissions, auth, data access, or runtime execution context
Use the suggested angle to start with a specific surface area, not fear-based messaging
Keep reference projects separate from emerging projects before outreach

Buyer note: LLM gateway

Use the radar to identify teams and tools that are standardizing model access or abstracting multiple providers.

Look for routing, fallback, policy, spend, or provider-switching language
Pair public activity signals with source links before deciding whether a record is outreach-ready
Use watchlist entries for market mapping even when the contact path is not ready

Buyer note: AI observability

Use the radar to spot workflows that may need traces, evals, replay, feedback loops, or debugging visibility.

Review agent frameworks, RAG apps, workflow builders, and eval-adjacent tools
Focus on evidence that a workflow is becoming repeatable or production-like
Use the fit note to separate monitoring relevance from generic AI activity

How to use this radar

Treat the public issue as a research checklist, not as a ready-to-send contact list.

Pick one buyer lens first: AI security, LLM gateway, observability, consulting, or investor research
Open the public source before contacting anyone
Use source-check status to decide whether a record is watchlist, manual-review, or export-ready
Use the suggested angle only after your team confirms the project is relevant

What paid Monthly Radar includes

Paid Monthly Radar is a recurring public-source research delivery during beta, with buyer verification and human review boundaries kept visible.

A scoped weekly or monthly brief for one confirmed buyer category
CSV, Excel, or JSON records with source links, evidence, timing notes, fit notes, fit scores, suggested angles, and source-check status
AI Agent Usage Signal, Security Risk Signal, Tool Stack Signal, Hiring / Budget Signal, Best Outreach Angle, and Suggested Email
Watchlist additions separated from records promoted for manual review
Manual notes on category movement and why specific records were included or held back

What Monthly Radar does not include

The paid radar is research context, not an automated outbound or guaranteed-outcome product.

No private emails, guessed emails, scraped inbox data, or phone numbers
No automated sending, CRM automation, login system, database, or payment flow
No guaranteed replies, meetings, customers, or revenue outcomes

Beta delivery boundary

The first radar sample should prove whether the category and format are useful before anything recurring is sold.

Start with one narrow category and clear exclusions
Review the free sample format before requesting a paid monthly scope
Keep public-source evidence and manual verification visible in every delivery

Manual request workflow

Request sample leads, a custom category, or a Weekly Radar review

AgentInfra Radar is currently a beta research workflow. Each request is reviewed manually so the target category, delivery format, and research boundaries are clear before any paid work.

Request via #signup

Email fallback

If your browser does not open an email draft, copy the prepared request and send it from your normal work inbox.

Copy request details

The form prepares a concise note with your category, company, and requested sample type for human review.

Beta delivery scope

Sample packs and radar reviews are delivered as public-source research files, not automated outreach or guaranteed outcomes.

Next step

Request weekly radar sample

Send the buyer category you care about and ask for a sample issue in this format. The first response is manual so scope and boundaries stay clear.

Request weekly radar sample