#112 — A formula for building effective AI agents

Workflows are ideal for structured, predictable tasks where the decision tree is clear and the cost of error needs to be tightly managed. For example, an e-commerce startup automated 75% of customer service queries by mapping explicit FAQ decision trees, saving $7,200/month in LLM costs. Only their complex, ambiguous cases were handled by agents.

Key risks include unpredictable cost overruns, latency spikes, and high-stakes errors that are hard to discover. For instance, a fintech firm’s agent once initiated incorrect fund transfers due to a poorly scoped prompt—in production, that meant an urgent rollback and tighter human-in-the-loop safeguards. Always assess error impact and cost-management strategies before giving agents autonomy.

Track every token, tool call, and latency in your agent’s workflow. Let’s say your SaaS agent workflow averages 45,000 tokens per task at $0.003 per 1,000 tokens: that’s $0.135 a task. Handling 10,000 tasks daily? You’re spending $1,350 a day. Many startups cut costs by first prototyping with agents, then hard-coding common paths as workflows to serve the 80/20 of use cases.

A healthtech app used agents to triage complex medical questions but switched to workflows for most common symptom checks. The result: agent cost dropped by 73% while coverage of standard queries improved, and clinicians still got value from the agent’s nuanced reasoning only where it was truly needed.

Think like your agent—test tasks using only the context window and available tools. Many founders use observability platforms to pipe agent prompts and tool calls back to a dashboard. For example, a productivity app founder caught a bug where the agent misunderstood calendar invite formats by simulating the process with only the data the agent had. Logging every step made the fix fast and repeatable.

Set task-level budgets for tokens and execution time, then enforce fail-safes. For example, an insurance startup set a 20,000-token cap per agent task; exceeding that triggered an alert and routed to fallback workflow automation. Open source tools like LangSmith or custom dashboards can monitor budget compliance in real time.

A logistics SaaS company ran multiple specialized agents (pricing optimizer, route planner, and compliance checker) that shared results through a simple message broker. This parallelization slashed customer quote response times by 65% compared to a monolithic agent and isolated context, avoiding cross-agent confusion—a model that’s gaining traction in vertical SaaS.

Inject your actual prompts and tool descriptions into your LLM (‘Claude’ or equivalent) and ask explicitly: 'What is ambiguous? What would make this easier to execute?' One founder realized their tool description for 'email draft' was missing allowed formats. Updating the prompt improved agent output quality by 30% in internal QA.

1. Token and tool usage per task (cost control)
2. Error rates and failure cases (especially silent fails)
3. User trust indicators (NPS, manual overrides, or ignored agent actions)

Asynchronous, specialized agent collaboration is set to unlock major workflow gains. For example, a travel marketplace deploying 'trip planner', 'flight finder', and 'deal optimizer' agents found faster custom trip results and fewer bottlenecks than their previous single-agent systems. Expect new SaaS models and best practices to emerge as agent-to-agent protocols mature in 2025.

Traditional automation and workflows follow set rules and predictable decision trees, ideal for repetitive tasks with clear inputs and outputs. AI agents, on the other hand, operate with greater autonomy, react to ambiguous contexts, and adapt strategies based on real-time feedback. For instance, a marketing SaaS workflow can automatically send drip emails, while an AI agent can analyze engagement patterns and craft personalized follow-ups in unexpected scenarios.

Founders need to weigh task complexity, cost/benefit analysis, error stakes, and verification ease. Only deploy agents for ambiguous, high-value tasks where workflows aren’t practical. For high-volume, predictable processes, stick to classic automation to save costs and reduce risks. Example: A fintech startup used agents only for fraud detection edge cases, but relied on workflows for daily payment processing.

Start with agents for rapid prototyping of new features, collect data on usage and performance, and then convert frequently used paths into rock-solid workflows. This hybrid approach helps manage costs and reliability. A real-world case: An HR platform prototyped interview scheduling with an agent, then automated 90% of cases with workflows, keeping the agent for unique, high-touch scenarios.

Leverage frameworks like LangChain, CrewAI, or open-source orchestration tools for agent development. Use observability platforms (e.g., LangSmith, Honeycomb, Amplitude) for real-time monitoring of token usage, routing logic, and error triage. Many founders use custom dashboards to visualize agent decisions and costs, enabling rapid iteration in production.

Be transparent with progress indications, provide easy user overrides, and show the agent’s reasoning when possible. For example, a SaaS ticketing tool increased adoption by showing agent steps and confidence scores, letting users review and accept agent actions—building trust while minimizing disruption.

The most common reasons are excessive scope and complexity, unclear cost models, insufficient guardrails against critical errors, and lack of real user feedback loops. Focus on launching minimal, tightly scoped agents, monitoring performance closely, and iterating based on real-world data for lasting success.

Implement explicit per-task or per-user budget caps and real-time alerts for cost overruns. Use agent telemetry to review token consumption by feature, and rework prompts and logic to be as concise as possible. A proptech startup saved over $10,000/month by refactoring prompts and switching common cases to structured automations.

Prompts should be clear, specific, and reviewable, avoiding ambiguity wherever possible. Toolsets should mirror user actions and expose only what’s necessary. In a logistics company, separating 'route optimization,' 'compliance check,' and 'client communication' as tools simplified agent logic and made human handoff seamless.

By delegating specialized roles (e.g., planner, executor, verifier) to different agents, startups can parallelize complex workflows, reduce context-window clutter, and insulate tasks from cross-contamination. A real estate SaaS split lead qualification, proposal drafting, and legal checks into agent subsystems, cutting onboarding times by 50%.

Include checkpoints for human review on high-stakes actions, provide clear explanations for agent decisions, and make manual overrides simple. For example, an insurance SaaS lets agents prefill claims which humans can verify with a single click, enhancing productivity without sacrificing trust or compliance.

Measure before/after on KPIs like cost per transaction, error rates, user satisfaction, and task latency. A B2B SaaS added agents for custom report generation, tracking a 40% reduction in manual labor costs and a 27% increase in upsell rates, while maintaining error rates below 0.5% through robust oversight.

Emerging standards and message brokers will allow asynchronous, specialized agents to cooperate seamlessly. Early adopters in travel, ecommerce, and logistics spaces already report faster task completion and greater resilience, as agents delegate subtasks and resolve dependencies in parallel—hinting at an 'API for agent conversations' as the future backbone for many startups.

Open source communities and AI infrastructure providers (like Hugging Face, LangChain forums, and case studies on Substack/Medium) regularly publish cost breakdowns and performance benchmarks. Startups should share anonymized metrics internally and with peers to calibrate expectations and discover optimization strategies tailored to their own workloads.