Introduction: The Quick Answer
You can build AI agents that coordinate tasks, access external tools, and learn from existing business contexts. This guide shows you how to do this using the latest open-source frameworks and prompt engineering techniques.The Foundations of an Effective AI Agent for Businesses
A productive agent is built on three essential components:1. Prompt Engineering: The Core of Communication
A well-structured prompt defines the purpose, context, steps, and constraints. Use clear language and explicitly reference the desired outputs.2. Access to Resources: Tools and APIs
Agents must securely access external services (CRM, databases, web services). Essential elements include API key management, rate controls, and tool wrappers.3. Memory and Continuous Learning
Short- and long-term memory enables the agent to maintain session state, recall previous decisions, and adapt to new data without losing context.Tools and Frameworks of 2026
The agent landscape is evolving rapidly. Here are three open-source projects that are reshaping the industry.Harness2: Google’s New Open-Source Framework
Google Research has released Harness2, an Apache-2.0 framework that rewrites an agent’s harness at test time. A single refinement improved GLM-5’s performance by 11.5%, making it ideal for rapid prototyping and iteration.Kyutai REKI: A 2.4B VLA for Robotic Manipulation
Kyutai, in collaboration with Inria and École des Ponts, has introduced REKI, a 2.4B Vision-Language-Action model that excels in robotic manipulation thanks to efficient memory. It can be integrated into business workflows to control IoT devices or production processes.TinyFish: Free Search for Agents
TinyFish eliminates retrieval costs for agents that read the web. It uses an optimized retrieval engine that reduces both API calls and downstream processing, making knowledge acquisition affordable at scale.Practical Example: Building an Expense Approval Agent
This agent checks an expense sheet, verifies policies, requests approval, and records final decisions. Below is a concise example using Harness2 and a structured prompt.# Pseudo-code for an expense approval agent
import harness2 as hb
# Define the agent's prompt
prompt = """
You are a corporate finance assistant. Analyze the expense {amount} for the category {category}.
Rules:
- Food > €50 requires manager approval.
- Travel > €200 requires CFO approval.
- Other expenses > €500 require CEO approval.
Return: 'APPROVED', 'REJECTED', or 'PENDING APPROVAL' with a brief justification.
"""
# Initialize the agent with access to the accounting system
agent = hb.Agent(
model='glm-5',
tools=[expense_api, policy_db],
memory=hb.Memory(short_term=10, long_term=True)
)
# Perform the check
def check_expense(amount, category):
response = agent.run(prompt.format(amount=amount, category=category))
return response
"""Best Practices and Deployment Tips
- Write clear and specific prompts.Use named sections (Purpose, Context, Steps, Output Format) to reduce ambiguity.
- Version your agents.Track changes to prompts and models to reproduce results and manage rollbacks.
- Test in sandbox.Use Harness2 to simulate edge cases before releasing to production.
- Monitor and log.Track tool calls, latency times, and deviations from expected results.
- Set limits and controls.Apply usage quotas, permission controls, and audit trails to protect business data.
Conclusion: Transform Your Workflows with AI Agents
Next steps:Choose a pilot project (e.g., expense approval), integrate a Harness2 agent, and start iterating with increasingly precise prompts.
IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present.
CURRENT CONTEXT (October 2026):
Here are some recent trends and news to inspire you:
- Google Open-Sources Harness2, a Recursive Agent Harnessing Framework: Google Research released Harness2, an Apache-2.0 framework that rewrites an agent's harness at test time. One refinement step lifted GLM-5 by 11.5% then... [2026-10-18]
- How to Make Zero-Training Tabular Predictions with NVIDIA Structured Data Models: In this tutorial, we implement Structured Data Models, NVIDIA’s new library that puts several pretrained foundation models for tables behind one PyT... [2026-10-18]
- TinyFish Makes Search Free: What It Really Costs an AI Agent to Read the Web: An AI agent that reads the web pays two kinds of cost. The first is the retrieval charge: what a search or fetch API bills per request. The second is mo... [2026-10-18]
Use this current information as inspiration to create an original and relevant prompt for 2026.