I’ve been testing Lindy Ai for a few days and I’m confused about what it can and can’t do in real-world workflows. Sometimes it handles tasks smoothly, other times it seems to miss obvious context or gives partial answers. I’m trying to figure out if I’m using it wrong, if there are specific prompts or setups that make it more reliable, or if there are known limitations I should work around. Can anyone share practical tips, best practices, or examples of how you’ve successfully integrated Lindy Ai into your daily work?
Short version. Lindy is good at structured, well-defined workflows. It falls apart when the task, context, or tools are fuzzy.
Here is what is going on under the hood in practical terms, based on how tools like it work.
- How Lindy “thinks”
- It runs on top of an LLM.
- On each step it:
- Reads your instructions, previous messages, and some memory.
- Decides to call tools (email, calendar, docs, APIs) or write text.
- Sends a new request to the model with the updated state.
- There is a token limit. Once the context gets long, some earlier stuff drops out. That often explains “forgot obvious context”.
- Why it feels smart sometimes
You get smooth runs when:
- The task is narrow.
- Example: “Summarize this 10 page doc in 5 bullets for an executive.”
- Tools are clear.
- Example: “Search this folder in GDrive, find all files with X in the title, make a table.”
- You give explicit constraints.
- “Ask me before sending any email.”
- “Use this template and never change headings.”
Then the model has fewer decisions. Fewer decisions means fewer ways to drift.
- Why it fails in “real” workflows
Issues I see a lot:
a) Vague top level goal
- Prompt: “Help me manage my sales pipeline this week.”
- Lindy needs:
- CRM source, fields, stage definitions.
- Decision rules for followups.
- Email style rules.
- Without that, it starts guessing. You get partial or generic output.
Fix:
- Turn 1 big goal into a working spec.
- “Use HubSpot via X integration.”
- “Focus on deals in stage ‘Proposal Sent’.”
- “Summarize status in a table with Deal, Amount, Stage, Next Step.”
- “Draft followup email for each deal, do not send without my approval.”
b) Hidden context in your head
You know the project. The model only sees text.
Example:
- You say: “Update the onboarding SOP to reflect our new policy.”
- You never gave the new policy in the same chat or in linked files.
- Model hallucinates or uses old info.
Fix:
- Link or paste all needed references in the same task.
- Use “source of truth” docs and always point to them.
- When policies change, say:
- “Forget older policy X. New policy is: … Use only this from now on.”
c) Tool use is flaky
If Lindy uses actions like:
- Send email
- Read calendars
- Call APIs
Then typical failures:
- API rate limits.
- Tool auth expired.
- It misinterprets tool responses.
You see:
- Partial runs.
- No followup after an obvious next step.
- Wrong data source.
Fix:
- Test each tool with a tiny task first.
- Example: “List the next 3 events from my Google Calendar.”
If that breaks, the bigger workflow will fail too. - After you set a workflow, log a few runs and inspect:
- What actions were taken
- Where it stopped
- How to structure prompts so it behaves better
Patterns that help a lot:
Pattern A: “Role + Scope + Sources + Output + Guardrails”
Example:
“You are my recruiting coordinator.
Scope: Only handle screening emails for SWE candidates.
Sources: Read candidates from this sheet: [link]. Use the ‘Status’ column as truth.
Output: For each candidate with Status = ‘Screening’, write a reply email based on this template: [paste].
Guardrails:
- Ask me to approve before sending anything.
- Do not edit the sheet.
- If data seems missing, stop and ask.”
Pattern B: Step by step, explicitly
Instead of:
“Fix this workflow for onboarding clients.”
Use:
- “Read this doc and summarize the current onboarding steps in a numbered list.”
- “Identify gaps or inconsistencies.”
- “Propose a new version in clear steps, mark changed steps with [CHANGED].”
- “Then wait for my review.”
You keep control. It stops before going too far.
- Why it sometimes misses “obvious” context
Common reasons:
- Context fell out of the token window.
- It misread a vague line.
- Two conflicting instructions:
- Earlier: “Always use formal tone.”
- Later: “Be casual with this client.”
The model may weight one more than the other depending on ordering.
You can:
- Restate key rules in each new “big” task.
- Use short, explicit bullets instead of long narrative.
- Avoid mixing many different subprojects in one thread.
- What workflows tend to work well
From people testing these tools a lot, these are more reliable:
- Document tasks:
- Summaries
- Drafts based on strict templates
- Redlines and comparisons
- Simple operations processes:
- Turn support tickets into tags and priorities.
- Create daily reports from logs or sheets.
- Personal admin:
- Cleaning notes
- Generating agendas
- Organizing tasks into projects
Stuff that fails more often:
- Long multi-day sequences with lots of branching logic.
- Anything that needs rich world knowledge plus up to date company policies plus tool actions all mixed together.
- How to debug when it gives partial answers
When you see partial or weird output, try:
- Ask: “Explain what steps you tried, what tools you used, and where you stopped.”
- Often you will see:
- “Tried to call integration X, got error.”
- “Did not find file named Y.”
- “Stopped because of missing instructions.”
Then either:
- Fix the tool setup.
- Tighten your instructions.
- Break things into smaller steps.
- Practical next moves for you
Given what you wrote, here is a simple way to push it:
- Pick 1 real workflow that hurts you but is repeatable.
Example: “Every morning, turn yesterday’s Slack standup into a task list in Asana.” - Write a small spec with:
- Where to read from.
- Where to write to.
- Output format.
- Hard rules on tone and actions.
- Run it for 3 days.
- Each failure, ask it “What went wrong” and refine instructions.
You get a feel pretty fast for what it handles well and what belongs in “human only” or “human supervised with AI help”.
Think of Lindy as a very eager junior ops person with mild amnesia and no real-world intuition.
@cacadordeestrelas already nailed the “structured vs fuzzy” angle, so I’ll hit a different side of it: why it behaves erratically even when you feel like you’re being clear.
A few practical gotchas I’ve run into:
- “Memory” is not what you think
Lindy doesn’t have stable, global memory the way a human does.
There’s:
- Short-term context: whatever fits in the token window for this run
- “Memory” features: pinned notes, docs, CRM data, etc.
The catch: it does not automatically treat every prior thing you ever said or uploaded as always-relevant truth. Unless something is:
- directly in the current context window, or
- explicitly referenced as a source of truth
it’s basically “maybe I’ll remember, maybe I won’t.” That’s a big reason it “forgets obvious context.” That context is obvious to you, not to a statstical text engine juggling limited tokens.
- It’s not actually planning long-term
Real-world workflows feel natural to you, because you have a mental model like:
“I want Lindy to help with hiring across the next 2 weeks:
source candidates, email them, track status, remind me, etc.”
Lindy is not truly holding a 2-week plan. It’s doing:
- One step
- Look at state + instructions
- Decide next tool or text
- Repeat until something breaks or the task “looks done”
So when you say, “Help me manage X this week,” you’re implicitly asking it to:
- design the workflow
- operate the workflow
- monitor the workflow
- adapt to new info
That’s extremely brittle, because the system is not a workflow engine first, it’s a text engine glued to tools. You usually need:
- You design the workflow and checkpoints
- Lindy executes specific pieces inside it
- It is terrible at reading your mind about importance
One thing I slightly disagree with @cacadordeestrelas on is the idea that “just be more explicit” always solves it. In practice, even with decent instructions, Lindy still:
- overfocuses on surface details
- underfocuses on what you care most about
Example:
You say:
“Draft followup emails and update my CRM tasks. The CRM must be accurate.”
Lindy might:
- Produce gorgeous emails
- Update half the tasks
- Miss 1 status field that’s actually critical for your pipeline
To it, that’s a tolerable miss. To you, that’s the part that actually matters. It doesn’t have an internal sense of “this field is mission critical and I must never screw it up.” You have to artificially create that, like:
- “If you are unsure about any field, STOP and ask.”
- “Do not change fields X, Y. Only change field Z under condition A/B/C.”
- “If you update more than 10 records, summarize exactly what changed.”
- Tool calls feel “magical” but they are dumb pipes
When Lindy hits tools (email, calendar, CRMs, APIs), remember:
- It’s just assembling JSON / parameters based on text reasoning
- It doesn’t truly understand the tool’s semantics
So you can get:
- wrong IDs used
- mixing sandbox and prod environments
- overlooking an error message the tool returned in plain text
The partial outputs you see are often:
“Tool call failed or produced weird data, but the model didn’t fully understand the failure, so it just kinda… stopped.”
You can sometimes force clarity with followups like:
- “List every external tool you used, and for each, show: request, response, and what you inferred from it.”
When it spells that out, you’ll see where it quietly derailed.
- “Obvious context” is often hidden branching logic
In your head, a workflow might be:
- If the client is new, do A
- If they’re old, do B
- If they’re VIP, skip C and email me first
You might describe that once in prose. Lindy will try, but each branch:
- can conflict with others
- can be half-remembered later in the conversation
- can be interpreted in a too-generic way
If you want it reliable, you almost need to pretend you’re writing code, just in English:
- “If
ClientType = New, then do steps 1–3 and never 4–5.” - “If any field required for a branch is missing, do not guess; stop and ask.”
- “Before executing, restate which branch you picked and why.”
Is that annoying? Yes. But it turns the fuzzy “policy blob” into something the model can mechanically follow.
- Where it actually shines in “real” workflows
To keep this grounded, here’s where I’ve seen Lindy behave like a monster help, not a liability:
- “Take this inbound email, classify it, and suggest a reply template, but do not send.”
- “Read yesterday’s meeting transcript, extract action items into this task system, using this field mapping.”
- “Check this doc / sheet and tell me what is missing for [very specific outcome].”
- “Generate drafts for repetitive comms using a template that I lock down hard.”
Pattern:
It works inside your workflow, not as your workflow.
- If you want to push it further anyway
If your goal is to see where the edge really is, I’d try:
- Pick one workflow you’d actually rely on in your business
- Write it out like you were delegating to a new hire who might get fired if they screw it up
- Add explicit “stop and confirm” checkpoints, especially where money, clients, or data integrity is involved
- Run it for a week and log where you would have been pissed off if it had run unsupervised
Those “pissed off moments” are exactly where the current generation of tools breaks: ambiguous priorities, missing data, and multi-step logic stretched too far over time.
TL;DR:
Lindy isn’t broken, it’s just not the autonomous ops brain the marketing copy makes you unconsciously hope for. Treat it like a very fast, very literal assistant with limited memory and no common sense, plug it into specific slices of your workflow, and keep humans in charge of overall orchestration. That’s where it stops being random and starts being actually useful.
Think of Lindy AI less as “AI agent” and more as “configurable macro engine with an LLM in the middle.”
@techchizkid and @cacadordeestrelas nailed structured-vs-fuzzy and memory limitations. I’ll hit a different angle: system design, not prompts.
1. You’re probably asking it to own a “process,” but it’s only good at “moves”
Where people get burned is using Lindy like:
“Handle my podcast pipeline”
“Run my client onboarding”
That implies: track state, handle exceptions, remember who’s who, recover from errors, etc.
In reality Lindy is better at moves inside that pipeline:
- Take transcript → turn into show notes
- Take intake form → draft welcome email
- Take CRM list → generate followups
Disagreement with the other replies: I don’t think the main issue is your instructions being too vague. Even with very clean specs, today’s agents do not have a robust notion of “process boundaries.” They don’t know when to say “this is out of scope for this workflow.” So scope creep happens and quality craters.
Practical fix
Define your own process outside Lindy:
- You own the Kanban board or checklist
- Lindy only touches specific, labeled steps: “Lindy step” vs “Human step”
So your real workflow might be:
- Human: Decide pipeline priorities
- Lindy: Generate email drafts for Stage = Proposal Sent
- Human: Approve / reject drafts
- Lindy: Log outcomes in CRM, summarize day
That structure alone eliminates a lot of “partial answer” weirdness.
2. Stop chasing “memory,” build your own state
Another small disagreement: leaning too hard on Lindy’s “memory” features is a trap.
Instead of trusting:
- Pinned notes
- Conversation history
Treat Lindy as stateless, and you control state in:
- A “source of truth” doc
- A CRM / spreadsheet
- A project tool
Then always say things like:
- “Use this sheet as the only source of truth for contact status.”
- “Ignore anything we discussed before if it conflicts with this doc.”
You basically make Lindy re-derive context fresh each time from stable data. Feels wasteful, but it is way more predictable than “remember that thing I said last week.”
3. Where Lindy actually beats just using a raw LLM
You could ask: “Why bother with Lindy AI instead of just pasting stuff into ChatGPT / Claude?”
Pros of Lindy AI
- Tool orchestration baked in
It can schedule, email, hit APIs without you wiring everything yourself. - Repeatability
Once a pattern works, you can reuse it without retyping the mega-prompt. - Multi-step execution
It can, in theory, chain actions until a goal is satisfied, which generic chat models do only via you babysitting.
Cons of Lindy AI
- Opaque failure modes
Things silently stop after a tool error or misread response. You see “partial answer” and no clear reason. - Overconfidence in autonomy
The product framing makes you expect “agent” behavior, but what you get is more like a brittle RPA script plus LLM. - Harder to debug than a normal chat
You have to inspect tool calls, not just the text it outputs.
In other words, Lindy is great if you want “LLM + actions” in one place, but you pay in transparency and control.
4. How to make it survivable in “real” workflows
Building on what the others said, here are non-duplicate patterns that help:
A. Use external checklists Lindy must obey
Example:
Have a simple checklist in a doc:
- Read CRM
- Draft emails
- Log changes
- Create summary
Then prompt:
“Work through this checklist step by step. After each step, restate:
- Which checklist item you just completed
- What data you used
- What you produced
Stop if something is missing and ask me.”
Now you have:
- A debugging view
- A guard against it skipping from step 1 to 4
B. Add “health checks” to tasks
Instead of just “update CRM,” say:
“After you update CRM, run a consistency check:
- For every deal you changed, confirm that Stage and Next Step are both non-empty.
- If any row violates this, list them and stop.”
You turn “Please be careful” into a concrete, verifiable constraint.
5. Dealing with “missed obvious context”
What feels “obvious” to you is usually one of:
- Implicit preferences
(Which contacts actually matter, how aggressive followup should be) - Cross-thread info
(Something said last week in another chat, or in a call not transcribed) - Soft rules
(“Except VIPs, they’re different”)
Trying to encode all of that into one mega-prompt is a losing game.
Instead:
- Create a short “policy doc” for each domain: Sales, Hiring, Support.
- Keep it under 1–2 pages each.
- Link to the relevant one every time you start a task in that domain.
Lindy is decent at ingesting small policy docs each run; it’s terrible at recalling scattered rules from multiple old conversations.
6. Where @techchizkid and @cacadordeestrelas fit in
Both of them are basically arguing “be more structured, more explicit.” That is correct but only half the story.
The other half is architecture: deciding what Lindy is allowed to own.
- Let Lindy own: transformation tasks, drafting, simple state updates, classification.
- Keep for humans: prioritization, exception handling, customer-sensitive decisions, schema changes.
Think of it like hiring a smart intern who:
- Types fast
- Can read and transform huge docs
- Has no idea what actually matters to the business unless you spell it out
Use it accordingly and it stops feeling random.
7. Competitors and mental model
This pattern is not unique to Lindy. Any agent layer built on an LLM (including tools others talk about here) will show similar:
- Token limits
- Tool misreads
- Overconfident autonomy
Lindy’s real advantage is bundling “LLM + tools + workflows” in one product so non-engineers can play. Its real disadvantage is that this bundling hides complexity until something breaks.
So if you keep using Lindy AI:
- Treat your workflows as “LLM-assisted ops,” not “AI-run ops.”
- Design around statelessness and explicit state in your own systems.
- Wrap every important workflow with human checkpoints and health checks.
Used that way, it becomes a very strong assistant rather than a flaky “AI cofounder” that occasionally forgets what company it works for.