case study: deltagency vs autogen

the content creation factory showdown

NOTE: this page refers to a very early-stage development phase of the framework and it does not reflect its current state or its full capabilities

overview

this case study compares a task-first, ollama-native framework (deltagency) with microsoft’s autogen under identical conditions.

deltagency focuses on deterministic, task-based workflows with a lightweight, zero-bloat design and a relay pattern for orchestration, while autogen emphasizes conversational collaboration.

the analysis examines code complexity, resource management, execution patterns, and output quality to reveal unexpected insights about framework trade-offs and challenge common assumptions about enterprise versus custom solutions.

test structure

conditions

• same model: llama3.1:8b via runpod proxy
• same agents: researcher, writer, editor, seo_optimizer, publisher
• same system prompts: identical specialization instructions
• same task: “create comprehensive blog post about ‘ai automation trends in small businesses’”
• same infrastructure: same runpod gpu backend

key differences

• deltagency: relay pattern configured as a sequential pipeline for this test (researcher→writer→editor→seo→publisher); the framework also supports consultation loops and routing patterns
• autogen: round robin group-chat with max_turns=5

agents and task definitions

• model: llama3.1:8b
• researcher system prompt: “You are an expert content researcher. Your job is to gather comprehensive, accurate information on given topics. Focus on finding credible sources, key statistics, trending angles, and unique insights that will make content stand out.”
• writer system prompt: “You are a skilled content writer. Transform research into engaging, well-structured content. Create compelling headlines, smooth transitions, and maintain consistent tone. Focus on readability while preserving all important information from research.”
• editor system prompt: “You are a professional content editor. Review content for grammar, clarity, flow, and structure. Remove redundancy, improve readability, ensure logical organization, and maintain consistent style. Make the content polished and professional.”
• seo optimizer system prompt: “You are an SEO content specialist. Optimize content for search engines while maintaining readability. Add relevant keywords naturally, improve meta descriptions, suggest internal linking opportunities, and ensure content follows SEO best practices.”
• publisher system prompt: “You are a content publishing specialist. Format content for final publication, add appropriate call-to-actions, ensure proper formatting for the target platform, and create engaging social media snippets for promotion. Give the final polished document as result.”
• task: “Create a comprehensive blog post about ‘AI automation trends in small businesses’ that will rank well on search engines and drive engagement”

outputs

the outputs have been copied as-is.

deltagency output

Revolutionizing Small Businesses: The Latest AI Automation Trends to Watch

Meta Description: “Discover the latest AI automation trends transforming small businesses. From process optimization to customer service, find out how AI can boost your company’s efficiency and productivity.”

Introduction:

Artificial intelligence (AI) has been making waves in various industries for years, but its impact on small businesses is more significant than ever. As technology advances, small businesses are leveraging AI automation to streamline operations, improve customer experiences, and gain a competitive edge.

The State of AI Automation in Small Businesses

According to a recent survey by PwC, 62% of small business owners believe that AI will have a positive impact on their operations (PwC, 2020). This trend is expected to continue, with the global market for AI in small businesses projected to reach $14.8 billion by 2025 (MarketsandMarkets, 2020).

Trend 1: Automation of Repetitive Tasks

One of the most significant benefits of AI is its ability to automate repetitive tasks, freeing up staff for more strategic work. Small businesses can use AI-powered tools like process automation software and robotic process automation (RPA) to:

• Automate data entry
• Streamline invoicing and payment processes
• Enhance customer service with chatbots

Trend 2: Personalized Customer Service

AI-powered customer service is revolutionizing the way small businesses interact with their customers. With the help of natural language processing (NLP), businesses can:

• Offer personalized recommendations based on customer behavior
• Analyze sentiment and respond to customer queries in real-time
• Use AI-driven chatbots to provide 24/7 support

Trend 3: Predictive Maintenance

Predictive maintenance is a game-changer for small businesses with equipment or machinery. By leveraging machine learning algorithms, businesses can:

• Predict equipment failures and schedule maintenance accordingly
• Reduce downtime and improve overall efficiency
• Extend the lifespan of critical assets

Trend 4: AI-Powered Cybersecurity

Cyber threats are on the rise, and small businesses are increasingly vulnerable to attacks. AI-powered cybersecurity solutions can help:

• Detect anomalies and prevent cyber-attacks in real-time
• Analyze network traffic and identify potential vulnerabilities
• Provide actionable insights for proactive security measures

Implementing AI Automation: Challenges and Opportunities

While AI automation offers numerous benefits, small business owners may encounter challenges when implementing these trends. Some common obstacles include:

• Integration with existing systems: Small businesses may struggle to integrate new AI-powered tools with their legacy infrastructure.
• Data quality and availability: Poor data quality or limited data availability can hinder the effectiveness of AI-driven insights.
• Employee resistance: Employees may be hesitant to adapt to new technologies, requiring training and education.

Case Studies: Real-World Examples of AI Automation in Small Businesses

To illustrate the potential of AI automation in small businesses, let’s take a look at some real-world examples:

• Company A: A small retail business implemented an AI-powered chatbot to handle customer inquiries. As a result, they saw a 25% increase in sales and a 30% reduction in customer support requests.
• Company B: A small manufacturing company used predictive maintenance algorithms to reduce downtime by 40%. This resulted in significant cost savings and improved product quality.

Best Practices for Implementing AI Automation

To ensure successful implementation of AI automation, consider the following best practices:

1. Conduct a thorough analysis: Identify areas where automation can be applied and assess the potential benefits.
2. Invest in employee training: Educate employees on the use and benefits of AI-powered tools.
3. Monitor and evaluate effectiveness: Regularly monitor the performance of AI-powered tools and make adjustments as needed.

The Future of AI Automation in Small Businesses

As AI automation continues to evolve, small businesses must stay ahead of the curve to remain competitive. By embracing these trends and investing in AI-powered tools, small business owners can:

• Enhance efficiency and productivity
• Improve customer experiences
• Gain a competitive edge in their respective markets

Conclusion:

AI automation is revolutionizing the way small businesses operate. By leveraging AI-powered tools and staying informed about industry trends, small business owners can unlock new opportunities for growth and success.

Recommendations for Small Business Owners:

1. Invest in AI-powered tools: Leverage AI to streamline operations and improve customer service.
2. Develop an AI strategy: Create a tailored plan to integrate AI automation into your business operations.
3. Stay informed about industry trends: Regularly update yourself on the latest developments in AI automation.

Sources:

• Gallup (2020). “State of the American Workplace Report”
• PwC (2020). “Global Artificial Intelligence Study”
• MarketsandMarkets (2020). “Artificial Intelligence Market for Small Businesses”
• Statista (2022). “Number of small businesses using artificial intelligence in the United States”

Keyword density:

• AI automation: 1.5%
• Small business: 1.8%
• Artificial intelligence: 1.2%

Image suggestions:

1. An image of a person working on their laptop, with an AI-powered tool integrated into their workflow.
2. A graph illustrating the growth of the global market for AI in small businesses.
3. A photo of a customer interacting with an AI-driven chatbot.

FAQs:

• Q: What are the benefits of AI automation in small businesses? A: AI automation can enhance efficiency, productivity, and customer experiences while gaining a competitive edge.
• Q: How do I implement AI automation in my business? A: Conduct thorough feasibility studies, invest in employee training, and monitor the effectiveness of AI-powered tools.

Call-to-Action:

Ready to revolutionize your small business with AI automation? Contact us today to learn more about our AI-powered solutions and how they can benefit your company.

autogen output

Title: Revolutionizing Small Businesses: AI Automation Trends to Watch Out For

Meta Description: Discover the latest AI automation trends in small businesses, from chatbots to predictive maintenance. Learn how to harness AI’s potential and stay ahead of the competition.

Header Tags:

H1: Revolutionizing Small Businesses: AI Automation Trends to Watch Out for H2: The Rise of AI Automation: H3: Top AI Automation Trends in Small Businesses: H4: Benefits of AI Automation for Small Businesses:

This comprehensive blog post on “AI automation trends in small businesses” effectively incorporates the latest research findings, industry insights, and actionable tips to educate small business owners about the benefits and challenges of AI automation.

Strengths:

1. Clear Structure: The article is well-structured with clear headings, subheadings, and bullet points that make it easy for readers to navigate.
2. Actionable Insights: The author provides practical advice on how small business owners can implement AI automation solutions in their organizations.
3. Relevant Keywords: The use of relevant keywords such as “AI Automation,” “Small Businesses,” and “Chatbots” will improve the article’s search engine optimization (SEO).
4. Credibility Boosters: The inclusion of industry reports, statistics, and research findings adds credibility to the article.

Weaknesses:

1. Overemphasis on Trends: While discussing AI automation trends is essential, some points could be more in-depth or supported by additional data.
2. Lack of Examples: Adding specific examples or case studies would make it easier for readers to understand how AI automation can be applied in various contexts.

Recommendations:

1. Add Visual Content: Incorporate infographics, diagrams, or charts to illustrate complex concepts and provide a better visual representation.
2. Diversify the Examples: Provide more diverse examples of small businesses that have successfully implemented AI automation solutions.
3. Enhance Readability: Use simpler language in certain sections and break up long paragraphs into shorter ones.

SEO Optimization:

The article includes relevant keywords, meta description, header tags, and a clear structure to improve search engine rankings:

1. Keyword Density: The target keywords are used moderately throughout the article.
2. Meta Description: A concise summary of the article’s content is provided in the meta description.
3. Header Tags: Header tags (H1-H4) are used effectively to structure the content.

Engagement Strategy:

To increase engagement, consider adding:

1. Call-to-Action (CTA): Include a CTA at the end of the article encouraging readers to take action or explore further resources.
2. Social Sharing: Add social media buttons to make it easier for readers to share the article with their networks.
3. Additional Resources: Provide links to relevant articles, whitepapers, or webinars that offer more in-depth information on AI automation trends.

Here’s an example of how the revised content could look:

Revolutionizing Small Businesses: AI Automation Trends to Watch Out For

Did you know that by 2025, AI will be responsible for 40% of all digital business activities? According to a report by Gartner, this trend is expected to continue, with AI adoption rates increasing from 37% to 51% in the next two years alone. Small businesses are no exception, as they seek to capitalize on AI’s ability to automate repetitive tasks, enhance decision-making, and improve overall productivity.

The Rise of AI Automation:

As AI continues to transform the business landscape, small businesses must be proactive in harnessing its potential. By understanding the latest trends, benefits, challenges, and limitations, small business owners can make informed decisions about how to integrate AI into their operations.

Top AI Automation Trends in Small Businesses:

1. Chatbots and Virtual Assistants: Conversational interfaces have become increasingly popular in small businesses, enabling customers to interact with the company 24/7 without human intervention.
2. Predictive Maintenance: AI-powered predictive maintenance uses machine learning algorithms to analyze equipment data and predict potential failures before they occur.
3. Automated Customer Service: AI-driven customer service platforms use natural language processing (NLP) to provide personalized support to customers, freeing up human staff for more complex issues.
4. Streamlined Bookkeeping and Accounting: AI-powered accounting software automates tasks such as invoicing, expense tracking, and reconciliations, saving businesses time and reducing errors.
5. Cybersecurity Threat Detection: As small businesses increasingly rely on digital technologies, AI-driven cybersecurity solutions can detect threats in real-time, protecting against data breaches and other cyber-attacks.

Benefits of AI Automation for Small Businesses:

1. Increased Productivity: AI automation enables small businesses to complete tasks faster and with greater accuracy.
2. Improved Customer Experience: Personalized support and streamlined services lead to enhanced customer satisfaction and loyalty.
3. Enhanced Decision-Making: Data-driven insights from AI can inform business decisions, reducing risk and improving overall performance.
4. Cost Savings: By automating routine tasks, small businesses can reduce labor costs and allocate resources more efficiently.

Challenges and Limitations:

1. Data Quality and Availability: AI requires high-quality data to train models effectively. Small businesses may struggle with collecting and maintaining relevant data.
2. Technical Expertise: Implementing AI solutions often necessitates specialized knowledge, which can be a barrier for small business owners without in-house expertise.
3. Change Management: Integrating new technologies into existing workflows can be complex and requires careful planning to avoid resistance from employees.

Actionable Tips for Small Businesses:

1. Start Small: Begin with simple AI applications, such as chatbots or automated bookkeeping software, before scaling up to more complex solutions.
2. Invest in Data Quality: Ensure data is accurate, complete, and relevant to train effective AI models.
3. Develop an AI Strategy: Outline clear goals, resource allocation, and change management plans to ensure successful implementation.

Conclusion:

As AI automation continues to transform the business landscape, small businesses must be proactive in harnessing its potential. By understanding the latest trends, benefits, challenges, and limitations, small business owners can make informed decisions about how to integrate AI into their operations. Whether it’s streamlining customer service or optimizing bookkeeping processes, AI automation offers a wealth of opportunities for small businesses to innovate and stay ahead of the competition.

The revised content includes minor adjustments such as adding a hook in the Introduction, breaking up long paragraphs, using active voice, and varying sentence structure. This comprehensive blog post on “AI automation trends in small businesses” provides actionable insights for implementation, while minor adjustments enhance its readability and engagement potential.

architectural comparison

implementation

deltagency

# agent definition
researcher = (ExecutorAgent(MODEL, BASE_URL, RESEARCHER, desc)
           .with_system_prompt(RESEARCHER_SYSTEM_PROMPT))

# workflow definition
deltagency_relayer = (Relayer()
    .add_node(researcher, delegate_to=[writer])
    .add_node(writer, delegate_to=[editor])
    .add_node(editor, delegate_to=[seo_optimizer]) 
    .add_node(seo_optimizer, delegate_to=[publisher])
    .add_node(publisher, unload_after_use=True))

# task execution
deltagency_relayer.run(TASK)

autogen

# model client definition
model_client = OllamaChatCompletionClient(model=MODEL, host=BASE_URL)

# agent definition
researcher = AssistantAgent(name=RESEARCHER, 
                          model_client=model_client,
                          system_message=RESEARCHER_SYSTEM_PROMPT)

# workflow definition
autogen_chat = RoundRobinGroupChat([researcher, writer, editor, 
                                  seo_optimizer, publisher], max_turns=5)

# task execution
await Console(autogen_chat.run_stream(task=TASK))

philosophy

deltagency

• clear separation of concerns – each agent has one job
• controlled context handoff – agents pass the necessary state/results; relay pattern includes a clean step to release context
• unload optimization – final agent can unload the model to free memory
• linear debugging – easy to trace issues to a specific agent

autogen

• collaborative iteration – agents can respond to and critique each other
• max_turns limit – prevents infinite loops (set to 5 in this test)
• async-friendly execution – often run with an event loop for streaming; synchronous paths also exist
• distributed decision making – flow is powerful but less predictable

why deltagency differs (at a glance)

• task-focused deterministic workflows
• modular, composable agents
• ollama-native direct api integration
• lightweight runtime with minimal deps
• developer-friendly clean api

comprehensive performance comparison

workflow analysis

both frameworks follow the same 5 stages, but with different approaches.

stage	deltagency (sequential evolution)	autogen (conversational collaboration)
research	collects statistics and trends, builds a solid foundation	produces a comprehensive draft with statistics
writing	expands with challenges, opportunities, and implementation	adds strengths/weaknesses analysis
editing	enriches with case studies and real-world examples	focuses on readability (shorter, clearer text)
seo	adds best practices, keyword optimization, structured flow	adds hooks, active voice, breaks long paragraphs
publishing	final polish with faqs, cta, and social snippets; publication-ready formatting	suggests cta and social sharing; requires manual finalization for publication

execution results

aspect	deltagency	autogen
style	more articulated, like a long-form blog post	more schematic, concise, and seo-optimized
real data	yes, includes statistics and concrete case studies	no, only generic references
readability	dense, requires focused reading	smooth and more immediate
seo	meta + keyword density + suggested images	meta + headers + keywords, but lacks visuals
cta/engagement	strong final cta	suggested to add cta/social sharing
content depth	provides best practices, challenges, opportunities	focuses more on trends and recommendations
practical tips	general recommendations at the end	actionable step-by-step tips (e.g., “start small”)
examples/case studies	2 specific business examples with measurable results	lacks concrete examples, more theoretical
engagement strategy	cta + faqs + image suggestions	suggestions to add infographics and sharing options
target audience fit	best for readers seeking in-depth analysis and authority	best for readers who want a quick, digestible overview
overall use case	ideal as a reference article or pillar content	ideal as a fast seo blog or linkedin article

execution metrics

metric	deltagency	autogen	winner	margin
execution time	69.37s	57.42s	autogen	-17% faster
words/second	12.61	17.41	autogen	+~5 words/second
total words	~875	~1000	autogen	+~12.5% more content
sections completed	9	6	deltagency	+50% more sections
statistical citations	4 specific	2 generic	deltagency	2x more data

process characteristics

aspect	deltagency	autogen	winner	advantage
predictability	deterministic	variable	deltagency	consistent output
adaptability	rigid	flexible	autogen	dynamic adjustment
debugging ease	simple	complex	deltagency	linear troubleshooting
human-like process	mechanical	conversational	autogen	natural collaboration
scalability	excellent	moderate	deltagency	easy agent addition
learning curve	steep	gentle	autogen	faster onboarding

content quality matrix

element	deltagency score	autogen score	winner	notes
depth of research	very strong	moderate	deltagency	concrete stats vs generic
professional polish	highly polished	basic	deltagency	enterprise-ready
readability	good but dense	smooth and accessible	autogen	more conversational tone
seo optimization	advanced and precise	limited	deltagency	calculated precision
practical examples	rich and quantified	scarce and abstract	deltagency	quantified case studies
engagement factors	strong (cta + faq + social)	moderate (suggestions only)	deltagency	complete engagement features

workflow efficiency

process aspect	deltagency	autogen	winner	reasoning
setup complexity	minimal	moderate	deltagency	env vars vs complex config
configuration effort	low	high	deltagency	system prompts vs framework setup
maintenance overhead	very low	moderate	deltagency	simple codebase vs framework updates
quality consistency	excellent	variable	deltagency	deterministic pipeline
customization flexibility	excellent	limited	deltagency	direct prompt control
team collaboration	individual	group	autogen	natural peer review
cost efficiency	free	expensive	deltagency	local models vs api costs

limitations

disclaimer

it is important to note that deltagency is still in a super beta stage.

this means the results presented here should be read as exploratory and not as definitive benchmarks. several factors limit the generalizability of the comparison:

• the framework is under active development and not yet open-sourced; results reflect a snapshot before production stability
• the framework has not yet been optimized for performance or resource efficiency
• results are based on a single content generation task and may vary in other domains
• debugging and logging features are still minimal, which may affect reliability in larger tests
• no external validation has been performed, so conclusions are based solely on this controlled experiment

these limitations highlight that while the comparison offers useful insights into tendencies and trade-offs, the findings should not be taken as final evidence of superiority in production environments. instead, they provide a snapshot of how the two frameworks behave under equal conditions at this stage of development.

honest autogen advantages

autogen shows clear strengths in several areas that are worth acknowledging:

• peer review process: agents critique and refine each other’s output
• enterprise integration: natural fit for teams already in the microsoft ecosystem
• conversational development: interaction style may feel more natural to some teams
• cloud scalability: strong option for organizations with sufficient budget for token costs

setup complexity comparison

aspect	deltagency	autogen (ref impl.)	winner	reality
configuration	model and base url passed in code	model and base url passed in code	draw	both can read from env or constants
code required	compact pipeline: executoragent + relayer + run	compact pipeline: client + agents + groupchat + run	draw	similar amount of code
dependency management	deltagency runtime + model backend	autogen runtime + model backend	draw	both need a client lib and an ollama-compatible backend
deployment complexity	needs a reachable model backend	needs a reachable model backend	draw	identical requirement at this level
customization effort	edit per-node system prompts	edit per-agent system messages	draw	prompt-first customization on both
debugging difficulty	linear node chain via relayer	intertwined group chat turns	deltagency	easier causal tracing from node to node
context handling	full history forwarded downstream	shared conversation state across agents	draw	different mechanisms, comparable effect
resource control	explicit unload_after_use on final node	managed by framework runtime	deltagency	explicit release hint present
async requirements	not required	event loop and streaming console	deltagency	fewer runtime prerequisites

discussion

quick comparison

the comparison highlights two distinct philosophies with clear trade-offs:

• output style
  • deltagency: long-form, structured, data-rich, enterprise-like
  • autogen: shorter, smoother, more conversational and more readable
• workflow behavior
  • deltagency: sequential, deterministic, easy to trace
  • autogen: iterative, collaborative, harder to predict
• strengths
  • deltagency: depth, concrete examples, stronger seo structure
  • autogen: readability, speed, natural peer-like review
• weaknesses
  • deltagency: rigid when configured as a strict sequential pipeline; consultation loops were not enabled in this test
  • autogen: shallow on data, fewer concrete case studies
• best fit use cases
  • deltagency: reference articles, pillar content, enterprise documentation
  • autogen: brainstorming drafts, seo blogs, fast social content

qualitative analysis

overall, deltagency aligns with a production-first task-based mindset, while autogen reflects a creative and exploratory approach. the choice depends less on raw performance and more on the intended content type and context of use.

deltagency vertical specialization

pros:

• each agent maintains specific expertise
• incremental quality output
• highly professional final result
• deterministic process control

cons:

• less flexibility during execution
• no feedback between previous phases
• more rigid process

autogen horizontal collaboration

pros:

• more human and reflective process
• integrated self-correction
• dynamic adaptability
• proactive problem identification

cons:

• dispersion of specialized competencies
• communication overhead
• less thorough results
• loss of deterministic control

personal reflections

after running both frameworks side by side, a few personal takeaways stood out:

• surprises
  • as expected, autogen produced more volume, but deltagency generated richer content
  • the relay pattern in deltagency felt more predictable than anticipated, close to a production pipeline
• frustrations
  • overall it is kinda impossible to not notice how quick it is to plug & play autogen once you know your way around it
  • with the strict sequential configuration used here, deltagency left little room for agents to challenge earlier steps
• satisfactions
  • seeing deltagency deliver professional, publication-ready structure in a beta stage was encouraging
  • autogen’s conversational dynamics yielded drafts that needed minimal copy editing
• future tests
  • enable consultation loops and routing in deltagency to test adaptability
  • profile resource usage and latency across longer documents and different models
  • evaluate hybrid pipelines that inject targeted peer-review stages
  • replicate the experiment across multiple topics and audiences to check generalizability

these reflections underline that the experiment was less about finding a winner and more about understanding how different philosophies of agent collaboration shape the final result

conclusion

• deltagency
  • task-based with strict role separation
  • deterministic flow ensures traceability and consistent quality
  • best fit for structured tasks, long-form content, and enterprise use
  • excels when predictability and accountability matter most
• autogen
  • strong in readability and speed
  • collaborative iteration feels natural, almost like peer review
  • best fit for brainstorming, quick drafts, and exploratory work
  • excels when adaptability matters more than precision

in essence: autogen is conversation-first and favors flexible collaboration; deltagency is task-first and relay-driven, favoring controlled execution with predictable outcomes.

this was honestly quite a journey.

mainly because I had never written something like this before, nor had I run these types of tests with so many different factors in mind.

i hope to bring more case studies in the future, as I enrich deltagency with more and more features before releasing it and open-sourcing it.