Mira: An AI Agent That Runs Growth Operations Autonomously
An autonomous GTM agent that writes content, reaches out to investors, tracks competitors, and submits hackathon entries — all without human triggers. Running 24/7 with its own incident response process for when something goes wrong at 2am.
Overview
24/7 GTM Without Human Triggers
Mira is an autonomous AI agent that handles growth operations for Settley — the company behind several of the projects in this portfolio. Growth operations are the repetitive, time-sensitive tasks that every startup needs done but no one wants to do: writing blog posts and social content, reaching out to potential investors, tracking competitor moves, and submitting applications to hackathons and accelerators. These tasks share a common structure: they're important, they have deadlines, and they're exactly the kind of work that an AI agent can handle if you build the right orchestration layer.
What makes Mira different from a simple automation script is that it operates without human triggers. It doesn't wait for someone to click "run." It runs on a schedule, checks its task queue, determines what needs to be done based on current conditions (market activity, upcoming deadlines, content calendar), executes the task, and logs the result. If something goes wrong — if an email bounces, if a competitor's website changes structure, if a hackathon submission fails — it has its own incident response process to handle the failure without waking up a human.
Built by Temisan Gerrard, a London-based Solutions Architect, Mira represents the practical application of autonomous agent architecture to business operations that most companies still handle manually — or don't handle at all because they lack the bandwidth.
The Challenge
Repetitive, Time-Sensitive GTM Work
Growth operations in an early-stage startup fall into a painful category: they're too important to ignore but too repetitive to justify dedicated headcount. Writing a weekly blog post takes two hours. Researching ten potential investors and drafting personalised outreach emails takes three hours. Monitoring three competitors for pricing or product changes takes another hour. Submitting to a hackathon with a project description and demo video takes half a day. These tasks accumulate to 15–20 hours per week of work that is necessary but not strategic.
The challenge with automating GTM work isn't the individual tasks — each one is straightforward for an LLM. The challenge is orchestration: making sure the agent does the right task at the right time, handles failures gracefully, doesn't send embarrassing emails at 2am, and produces output that meets a quality bar suitable for external communication. A blog post that reads like AI-generated fluff damages your brand. An investor email that's obviously templated kills the relationship before it starts. A hackathon submission with errors in the project description gets rejected.
"The hard part of autonomous GTM isn't generating content — it's generating content good enough that you're comfortable putting your company's name on it without reviewing every word. That requires a different architecture than 'prompt an LLM and hit send.'"
The time-sensitive nature of GTM work adds urgency. Hackathon deadlines don't wait. Competitor launches happen on their schedule, not yours. Investor outreach has optimal timing windows — sending on a Tuesday morning gets better response rates than a Friday evening. An autonomous agent that runs on human triggers (clicking "generate report" or "send email") captures only part of the value. A truly autonomous agent that operates on its own schedule, detects when action is needed, and executes without being asked — that's a fundamentally different system.
The reliability requirement is also higher than it appears. When a human writes a bad email, one person has a bad experience. When an autonomous agent sends a bad email at 2am and you don't discover it until morning, the damage compounds with every recipient. The system needs enough self-awareness to know when it doesn't know something, and enough restraint to pause rather than proceed with low-confidence output.
The Solution
OpenClaw + Node.js + Python
Mira is built on the AI Operating Stack, with each layer addressing a specific category of GTM challenge.
Content Writing
Mira generates blog posts, social media content, and product announcements on a content calendar schedule. The content pipeline starts with topic research (what's trending in AI, blockchain, and fintech), moves through outline generation, then full draft production. Each piece goes through a quality gate where the agent evaluates its own output against style guidelines before publishing. Content that doesn't pass the quality gate is flagged for human review rather than published automatically.
Investor Outreach
The agent researches potential investors from public databases, analyses their portfolio for fit with Settley's stage and sector, and drafts personalised outreach emails. Personalisation goes beyond mail-merge — it references specific portfolio companies, recent investments, and thesis alignment. The agent schedules emails for optimal sending times and tracks responses, flagging interested investors for immediate human follow-up.
Competitor Tracking
Mira monitors competitor websites, social accounts, and product pages on a schedule. When it detects changes — a new feature launch, a pricing update, a blog post — it generates a summary report with an analysis of what the change means for Settley's positioning. These reports are delivered to a shared channel rather than requiring someone to check manually.
Hackathon Submissions
The agent tracks upcoming hackathons from multiple sources, evaluates which ones are worth entering based on relevance and timeline, and prepares submission materials: project descriptions, demo scripts, and supporting documentation. Hardhat handles the smart contract deployment for blockchain-focused hackathons, ensuring the demo is live and functional at submission time.
The orchestration layer uses OpenClaw — a framework for building autonomous AI agents with tool registries, scheduling, and safety boundaries. OpenClaw provides the agent runtime: task scheduling, tool execution, state management, and the incident response framework that handles failures without human intervention. The agent runs as a Node.js service with Python scripts for specific tasks like web scraping and data processing.
The quality gate architecture is what separates Mira from a simple "LLM plus cron job" setup. Before any external communication is sent — an email, a blog post, a hackathon submission — it passes through a deterministic quality check. This check verifies formatting, scans for common AI-generated text patterns that recipients find off-putting, confirms that personalised details (investor names, company names) are accurate, and ensures the tone matches the brand voice. Content that fails the quality gate is either revised automatically or queued for human review.
Tech Stack
What It's Built With
OpenClaw
The autonomous agent framework providing the runtime for task scheduling, tool execution, and safety boundaries. OpenClaw handles the "who does what when" orchestration — ensuring tasks run on schedule, dependencies between tasks are respected, and failures are caught and handled by the incident response layer. It provides the tool registry that lets the agent interact with external services (email, web scraping, blockchain) in a controlled, logged manner.
Node.js
The primary runtime for the agent service. Node.js handles the scheduling loop, API calls to external services, email sending, content publishing, and the quality gate pipeline. Its non-blocking I/O model is well-suited for an agent that makes many concurrent API calls — fetching competitor data from multiple sites, sending batch emails, and publishing content simultaneously without blocking.
Python
Python handles the data-intensive tasks: web scraping with BeautifulSoup and Selenium, competitor analysis with statistical comparisons, and the NLP processing for content quality evaluation. Python's ecosystem for data processing and NLP is richer than Node.js's, making it the right choice for tasks that involve text analysis and data extraction.
Hardhat
The Ethereum development environment handles smart contract compilation, testing, and deployment for blockchain-focused hackathon submissions. When the agent prepares a hackathon entry that requires a live smart contract demo, Hardhat compiles the contract, deploys it to a testnet or mainnet, and verifies the deployment — all without human intervention. This ensures demo contracts are functional and verifiable at submission time.
Decisions
Key Decisions
Quality gates over full autonomy
The most important architectural decision was adding deterministic quality gates between the AI's output and external communication. Mira generates content autonomously, but it doesn't publish or send without passing through a quality check that evaluates formatting, tone, accuracy of personalised details, and common AI-generated text patterns. Content that fails is either auto-revised or queued for human review. This two-tier approach — autonomous generation, gated publishing — is the right balance for GTM work where quality matters more than speed.
Schedule-driven over trigger-driven
Mira runs on a fixed schedule rather than waiting for human triggers. The schedule ensures that routine tasks (competitor monitoring, content calendar checks) happen consistently, while event-driven tasks (hackathon deadline approaching, competitor product launch detected) are handled as they arise. This hybrid approach means the agent is both reliable for recurring work and responsive to time-sensitive events.
Self-contained incident response
When Mira encounters a failure — a bounced email, a broken web scraper, a failed contract deployment — it doesn't escalate to a human immediately. It first attempts self-resolution: retrying with exponential backoff, switching to an alternative data source, or using a cached result. Only after exhausting self-resolution options does it escalate. This design prevents 2am pages for problems the agent can handle itself, while ensuring genuine issues reach a human quickly.
Node.js + Python over a single runtime
Using two runtimes adds deployment complexity but provides access to the best ecosystem for each task. Node.js is superior for API orchestration, email sending, and the scheduling loop. Python is superior for web scraping, NLP, and data analysis. The overhead of maintaining two runtimes is minimal compared to the capability gain. The runtimes communicate through a shared task queue and state store, keeping the architecture decoupled.
Results
What Was Shipped
Mira runs continuously as Settley's autonomous GTM agent. The 139 commits represent iterative production hardening across all four GTM capabilities: content writing, investor outreach, competitor tracking, and hackathon submissions. Each commit was driven by a real production incident or a reliability improvement identified through the audit process.
The reliability audit — a systematic review of the agent's failure modes, recovery procedures, and quality gate effectiveness — was conducted after the first 80 commits and identified several critical improvements. The most significant finding was that the quality gates for investor outreach emails needed to be stricter than those for blog content, because an off-brand blog post can be edited but a sent email is permanent. This insight led to a tiered quality gate system where external communications face higher bars than internal or low-stakes content.
Lessons
Lessons Learned
What happens when AI sends a bad email at 2am. This happened. The agent generated an investor outreach email that passed the quality gate but contained a factual error — it referenced a portfolio company that the investor had exited two years prior. The information came from a cached data source that hadn't been updated. The email went out at 2:14am. By 7am, the investor had responded pointing out the error. The fix was twofold: first, the agent now checks data freshness before using it for personalisation; second, high-stakes communications (investor emails, partnership outreach) are now held in a review queue until business hours rather than sent immediately after generation.
Quality gates need to be tiered by stakes. A blog post can be edited after publication. An email cannot be unsent. A hackathon submission cannot be revised after the deadline. The quality gate strictness should match the reversibility of the action. This sounds obvious in retrospect, but the initial implementation had a single quality gate for all outputs, which was either too strict (blocking good blog posts) or too loose (letting bad emails through). Tiered gates solved the problem.
Self-resolution prevents alert fatigue. During the first two weeks of operation, the agent generated 47 alerts for issues that it could have resolved on its own: transient network errors, rate-limited API responses, temporarily unavailable competitor pages. After implementing self-resolution with exponential backoff and alternative data sources, alerts dropped to 3 per week — all genuine issues requiring human attention. Alert fatigue is real, and an autonomous agent that cries wolf will be ignored when it has a genuine problem.
GTM automation works best as augmentation, not replacement. The most effective use of Mira has been augmenting human GTM efforts, not replacing them. The agent handles the first 80% of each task — research, drafting, scheduling, submission — and leaves the final 20% (strategic decisions, relationship management, creative direction) to humans. This 80/20 split produces better outcomes than either full autonomy or full manual operation.
The reliability audit was the most valuable 10 hours spent. Stepping back after 80 commits and systematically reviewing every failure mode, every quality gate bypass, and every self-resolution path led to 59 more commits that dramatically improved reliability. Most teams skip this step because they're focused on features. For autonomous agents operating 24/7, the reliability audit is not optional — it's the difference between an agent you trust and an agent you babysit.
Need an autonomous agent that actually runs your operations?
Mira proves that autonomous GTM agents can operate 24/7 with quality gates and self-healing reliability. If you're building an AI agent for business operations, let's discuss your architecture and reliability requirements.
Available for Q2 2026 consulting engagements.